#tech company org charts | Explore Tumblr posts and blogs

bidaubadeadieu · 2 years ago

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that post going around about why murdering one (1) exec of British petroleum is worth millions of tons of CO2 is so dumb and ppl are eating that shit up. I hesitate to respond to that post because I simply do not want to spread it, but no matter, you've heard arguments like this before. because I work on climate policy for a living, allow me to ask a few comprehension questions:

- why assume that the sudden death of a company official would decrease production of oil by 1% for a month? why not 0.5% or 0.25%? Whether there is any decrease and how big that decrease is are empirical questions, you can't eyeball it. The other scenario, reducing production by 25% for a day, is preposterous unless all the employees are taking a 2 hour mourning period.

- is this belief not inconsistent with the other commonly held belief on the left that CEOs are parasites and don't do shit? If value is derived from labor, do you honestly believe that 1% of BP's revenues (totaling over 100B each year) are attributable to one person? Even a few people?

- you can go online and search BP's org charts. BP has nearly 100 people with just the title "senior vice president", spread across a dozen business units like "innovation", "advocacy", "finance", "legal", and laughably, "sustainability". Anyways, which of these units contains the person you're going to shoot dead? How are you dealing with the fact that they have intentionally padded these groups to insulate from sudden shocks?

- the energy industry is, famously, characterized by inertia. The whole reason they are in this mess is due to their inflexibility. In a time of crisis, such as missing leadership, they're going to keep on chugging! The people who supposedly steer the ship are dead, and the people who actually know how to work the oilfields are still alive, couldn't that make transitional change less likely?

- ah yes! All those oilfields! BP has dozens of them, spread around the globe, filled with hordes of middle management. how, logistically, do you think that this change will happen? will it be that each worker presses buttons on the rig 1% more slowly? Or will it be that new oil sites are 1% slower to be sited and begin operation. These things employ thousands, operate sometimes for decades, and remember, they have production quotas to fill.

- what about demand? killing oil execs doesn't reduce the number of people trying to fill up their cars and keep the lights on, because oil consumption is largely inelastic. if production was lowered by 1%, the company will raise prices (just as they did during the pandemic) to maintain profit levels. In order to introduce elasticity to the market, we need real alternative choice in energy source and tech we use in our daily lives, which means subsidizing renewables, electrified transit, and regenerative agriculture, aka boring wonk shit when do I get to kill?

- this experiment has been and is already being run. In 1992 an Exxon exec was murdered and clearly that didn't solve anything. 30 years later, the guy that did it is still serving time in a prison in NJ. Russia has had a string of oil execs deaths lately for reasons I don't pretend to totally understand, but likely relating to the Ukraine war and exerting control, and no, they're clearly not worried about production declining or this hurting the Russian economy.

In short: No, this problem isn't fucking solvable by a well-placed bullet or two, or five.

#climate change #energy #policy

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kfposts · 15 days ago

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In 2020, the average manager had 5 direct...

In 2020, the average manager had 5 direct reports. Today? Some are managing 15, 20—even 50. But it’s not because of promotions or pay raises. It’s the result of a growing trend: flatter org charts. As companies trim layers of middle management, those who remain are being asked to do more with more. One tech firm now expects VPs to manage 15+ people. A pharma company? Up to 50. Just two years ago, the average was five. And it’s not stopping with humans. AI avatars and digital agents are next in line to be “managed.” As Korn Ferry’s \@Wolfgang Bauriedel puts it: “Once firms start going down this path, they rarely go back.” �� https://krnfy.bz/3Hu6Tba

In 2020, the average manager had 5 direct...

In 2020, the average manager had 5 direct reports. Today? Some are managing 15, 20-even 50. But it's not because of promotions or pay raises. It's the result of a growing trend: flatter org charts. As companies trim layers of middle management, those who remain are being asked to do more with more. One tech firm now expects VPs to manage 15+ people. A pharma company? Up to 50. Just two years ago, the average was five. And it's not stopping with humans. AI avatars and digital agents are next in line to be "managed." As Korn Ferry's @[Wolfgang Bauriedel](urn:li:person:M3Wdbw24k3) puts it: "Once firms start going down this path, they rarely go back." �� https://krnfy.bz/3Hu6Tba

Korn Ferry Connect

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halbanese1 · 15 days ago

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In 2020, the average manager had 5 direct...

Korn Ferry Connect

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orgkonnectt · 29 days ago

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Tesla Org Chart: Uncover the Leadership Behind the EV Giant

As a global leader in electric vehicles, clean energy, and cutting-edge innovation, Tesla operates with a bold and complex leadership structure. Understanding how Tesla’s teams are organized offers a strategic advantage to professionals in sales, recruiting, consulting, or business intelligence.

The Tesla org chart by OrgKonnect breaks down the hierarchy of one of the world’s most influential tech-driven companies.

Why Use Tesla’s Org Chart?

Tesla’s success is driven by cross-functional collaboration between its engineering, software, manufacturing, and leadership teams. With this org chart, you can:

Identify key executives and decision-makers

Understand departmental hierarchies across global operations

Target specific roles for enterprise sales, recruitment, or partnerships

Gain insight into reporting structures and lines of authority

Tesla Org Chart Highlights:

CEO & Executive Leadership

Engineering & Vehicle Programs

AI & Autopilot Development

Battery & Energy Solutions Teams

Manufacturing & Gigafactory Operations

Finance, Legal & Corporate Strategy

Each of these segments features key leaders and managers who shape Tesla’s innovation ecosystem.

Final Thoughts

The org chart Tesla provides a strategic lens into one of the most disruptive companies in the world. With OrgKonnect, you can navigate this structure to pinpoint the right connections and optimize your outreach, research, or analysis.

🔗 Visit the Tesla org chart now and take your prospecting or market insights to the next level.

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xpressluna · 2 months ago

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Fired From Meta After 1 Week: Here’s All The Dirt I Got

Getting hired by Meta felt like winning the tech lottery. Getting fired one week later felt like I was just a pawn in a much bigger game. I didn’t walk away bitter—I walked away with my eyes open. Here's what happened behind the scenes, why I think it went down the way it did, and what you should know if you're chasing a job at Big Tech.

How I Landed the Job

Let’s rewind. I applied for a mid-level software engineering role at Meta earlier this year. After four rounds of interviews, including system design, behavioral, and a coding marathon, I was offered a six-figure position. It felt surreal.

I had prepped for weeks, practiced LeetCode problems, memorized design patterns, and polished every possible “Tell me about a time…” story.

When I got the offer, I thought the hardest part was over.

I was wrong.

Orientation Felt Off From Day One

The onboarding process was… cold. Corporate. Rushed.

They gave us all a "Meta Way" handbook, access to endless internal tools, and told us to start learning their stack. But something felt off:

I had no assigned project

My manager was slow to respond

I was told to “explore” and “get familiar” with the systems

I thought maybe it was just the calm before the storm.

Turns out, it was the storm.

The Layoff Email Hit Like a Truck

Exactly 7 days after my first login, I woke up to a Slack ping and an email: “We regret to inform you…”

I had been part of a silent, non-public round of layoffs—targeting new hires across departments. No performance review. No warning. Just cut.

My badge deactivated that afternoon.

What I Learned Before I Left

You’d think one week isn’t enough to learn much.

Wrong.

In my short time at Meta, I picked up a disturbing amount of inside knowledge that most outsiders will never see. Here's the dirt:

New Hires Are Low-Hanging Fruit

Meta (and other tech giants) often overhire aggressively during boom cycles. When the market tightens, they slash new, unintegrated employees first. It’s cheaper, faster, and less disruptive.

They gamble on fast onboarding—but if priorities shift, you're disposable.

Massive Internal Tool Bloat

Meta has a maze of internal tools—many of which are redundant or broken. I saw three tools doing the same thing, none properly documented. If you didn’t know someone internally, good luck finding out how to do your job.

Pressure to Look Busy Is Real

During onboarding, other employees hinted that visibility > productivity. As long as you posted updates, added comments in internal forums, and looked “engaged,” you were considered safe—even if your actual output was questionable.

Culture Is Friendly, But Political

People are nice on the surface—but there’s a quiet competitiveness that seeps in fast. I heard stories of people blocking promotions, taking credit for team wins, or even ghosting new hires to protect their own turf.

What I Did After Getting Fired

Once the shock wore off, I did three things:

Posted my story on LinkedIn – It went viral. I got messages from other Meta hires who had the exact same experience.

Started freelancing again – I realized I didn’t want to tie my fate to another corporate org chart.

Began documenting everything I learned – Hence this post. Final Thoughts: It Wasn’t Personal—But It Was Real

Getting fired from Meta after one week wasn’t personal. But it was revealing.

It exposed the cracks in Big Tech’s hiring culture. It showed me how fragile even the most “secure” job can be. And most importantly, it reminded me of a brutal truth:

You are not irreplaceable to a company—but you are to yourself.

So if you’re chasing a dream job in tech, keep this in mind: it’s not just about landing the offer—it’s about surviving what comes after.

TL;DR

Got hired by Meta → Fired in 1 week

No project, no warning, no explanation

Meta cuts new hires first to clean up hiring excess

Learned a lot about culture, tools, and corporate politics

Left more alert, not angry

#trending #heavy metal

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nikhilvaidya27 · 3 months ago

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From Workforce to Work-Source: Rethinking Organizational Structure

By Nikhil Vaidya, Founder – Prism HRC

In every leadership meeting I’ve been part of in recent years, one theme surfaces again and again:

“We need people who can move faster, think sharper, and adapt without needing a new org chart every quarter.”

But here’s the tension: Most companies are still structured around fixed roles, not fluid work. The result? Rigid hiring cycles, misaligned expectations, and an overreliance on job descriptions that expire faster than the roles they define.

At Prism HRC, we believe it’s time to shift our lens—from managing a workforce to building a work-source.

💼 The Traditional Workforce Model Is Showing Its Age

Organizational structures today are still largely built on:

Hierarchies

Fixed functions

Linear career paths

Permanent roles with long tenures

This model assumes stability. But business, as we now know, isn’t stable—it’s adaptive, nonlinear, and rapidly digitized. Most of the friction we see in MNCs stems from one reality: the way work is done has changed, but the way we organize and acquire talent hasn’t.

Roles are getting redefined every 6–12 months. Skill demands are fluid. Yet, org charts remain static.

🔄 From Workforce to Work-Source: What It Really Means

A work-source approach looks at talent as modular, strategic, and outcome-oriented.

It asks:

What work needs to be done?

What capabilities are required (not just roles)?

Can this be done internally, externally, or cross-functionally?

Who can we deploy based on skill rather than title?

This mindset unlocks several structural advantages:

Agile staffing for projects and transformations

Flexible career paths driven by learning, not ladders

Cross-pollination of ideas across functions

Smarter hiring through capability-based sourcing, not JD-based filtering

At Prism HRC, we’ve seen this mindset shift result in 40–50% faster team assembly times for critical initiatives—especially in product, tech, and strategy functions.

🧩 Why MNCs Struggle with This Shift

Legacy systems, bureaucratic approvals, and siloed talent ownership make this harder than it sounds. HR and business heads are often measured on role fulfilment, not work outcomes. But the cracks are showing.

Our clients have voiced frustrations like:

“We have the talent. We just don’t know where they sit.”

“We’re hiring externally for skills our own people already have.”

“Our best internal talent is bored, underutilized, or exiting quietly.”

These aren’t talent shortages. These are structural blind spots.

🛠️ Rethinking Structure Starts with Rethinking Talent Strategy

Here’s what we advise our clients at Prism HRC when moving toward a work-source model:

Map work, not just roles → Break down high-impact deliverables into skills and capabilities.

Build a skill cloud, not a static org chart → Know what’s available internally before going to market.

Use talent more dynamically → Create fluid pools—project-based, cross-functional, gig-style deployments within teams.

Hire for contribution, not chronology → Move beyond experience brackets and title inflation; focus on work-readiness.

Redesign performance systems → Reward based on project impact and skills leveraged, not just tenure or title.

🔍 The Consultant’s Role in Enabling This Shift

Transforming structure requires both strategy and sensitivity. At Prism HRC, we support this evolution by:

Diagnosing skill gaps versus role gaps

Redefining hiring criteria based on capability clusters

Designing talent maps that align people to business priorities, not just departments

Guiding CHROs and business heads to adopt skills-first talent architecture

This isn’t about eliminating structure. It’s about liberating your structure from rigidity.

🎯 Final Thought: From Headcount to Impact

In today’s world, the competitive edge doesn’t lie in how many people you employ. It lies in how intelligently you align skills with shifting work demands.

The future belongs to companies that stop counting workers and start activating work-sources.

And at Prism HRC, that’s the future we help our clients design—one skill, one structure, one mindset at a time.

📲 Connect with Prism HRC🔗 Website:Prism HRC 📸 Instagram: https://www.instagram.com/jobssimplified/?hl=en

#jobs #books #jobseekers #freshers #experience

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plotholes-and-spellingerrors · 5 months ago

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I think the thing is that, fundamentally, there's no such thing as running an "AI" locally. (I'm assuming that when you say "AI" you're referring to the "generative" models where a user makes a natural-language request, e.g. "re-organize my business", and the model outputs an org chart or something.) The problem is that these models require so much compute that you basically cannot run them on a personal computer. If you saw that post going around talking about Windows quietly turning on constant system snapshots to run it's AI assistant, the article pointed out that some features were only available on machines with specially beefed-up processors and memory, and even then, internet connectivity was vital because it was sending data back to Microsoft to be processed - that was part of the privacy/security risk, that images potentially displaying passwords, etc., were being stored on Microsoft servers. The point being, you need massive server farms to ingest, store, and analyze the data that these models are being built on - and it never ends, or the model becomes stale. And even once you have the model, calculating a response is also incredibly resource intensive. That's why every single Google AI result takes so much more water and electricity than the entirety of the actual search results combined.

I have shared the link before, but I am begging people to listen to the "Data Vampires" miniseries from Tech Won't Save Us. They make clear very early on that the reason Microsoft, Google, and Amazon are pushing AI so much is because they are data center companies: they make money every time a service uses their data centers. And you know where ChatGPT and MidJourney results are being processed? On Microsoft and Google and Amazon's data centers. In fact, a lot of the "money" that these companies are investing into GenAI orgs is actually just donating server usage to them, to get them locked in on those servers, which will then need to be paid for. So the GenAI companies have to find a way to get people to pay for AI services, because they're paying for AI compute power.

The other thing, and I cannot stress this enough, is that these are not knowledge models. They are very sophisticated duplication models. If you say to the "AI", "Make my workflow simpler", it doesn't have a semantic understanding of what "simpler" means (and it certainly can't ask for clarification). All it can do is look at its massive data set and try to predict a version of whatever it is outputting that is associated with the string of letters "simpler", based on a bunch of stuff that has previously been labeled as "simple" (or similar words). So you might get a work flow that has fewer steps, for example, but what the "AI" might not realize (because it doesn't have a concept of what any of the steps are) is that maybe it's actually added that are meaningless, maybe it's just shuffled some steps to it looks like there's fewer but actually it's just hidden a couple... or maybe it's eliminated some vital error-checking steps that 98% of the time result in nothing (it looks like a waste of time, so simplify it out) but 1.7% of the time catches something minor (oh good, that would have wasted resources to fix) and 0.3% of the time catches something mission-critical (this error could have brought us to bankruptcy/court/prison). And keep in mind, the person asking an automated system to simplify their business workflow is NOT the person who designed the workflow and knows what needs to be in there (if they were, they wouldn't need a computer to tell them how to fix it) - they're the person trying to not need to pay someone to design the workflow.

And for that matter, a lot of what happens when some "AI" system is inserted into a process is that it's still doing the same task (just at a much higher computational requirement, and potentially wrong), but it looks simpler because the human user is only clicking one button rather than five.

And look, I'm all for automating tasks that are repetitive and time-consuming for a human - that's the whole point of computers in the first place. But a truly effective and efficient replacement for human work is one that was designed by people who are intimately familiar with the required tasks and thinking deeply about how to make their jobs easier.

You know, these techbros love to make it sound like humans are just bad at making systems, computers will be so much better at it, but I think that's a lie they tell because they want to make one generic product and get everyone to buy it. That's the promise of "general artificial intelligence" - it's one system that can do everything. But what else in life works like that? Do you put your clothes and your dishes in the same washing device? Something that does a lot of things well is typically very simple. You can fry an egg and boil water in the same pan because it's a bent piece of metal sitting over heat. An espresso machine, however, will have a harder time with the egg. Good software generally focuses on doing one thing - the difference is whether that one thing is simple and can be applied to a lot of different things (Firefox renders web content, it just happens that you can have all kinds of stuff on the web) or if that one thing is extremely specific (Audacity edits music and GIMP edits images - audio and images are both data types that Firefox can show you, but can you imagine what a mess GIMPdacity would be?); the problem is that this specialization requires human effort, and these techbros do not want to PAY for human effort; the problem is that this specialization means that you have target audiences, and these techbros want to sell to a general (read: bigger) audience. And conversely, those byzantine systems that are so terrible? Often it's not because people are just dumb, it's because either the job is a very complicated one, and the system is tailored to success in that task, or because someone wanted a general system that accomplishes all kinds of things, and often what they want accomplished is METRICS so that the efficiency of the business can be micromanaged. But that's another story for another time. In short, don't ever let someone who is trying to sell you bullshit tell you the lie that you couldn't do better on your own.

Anyway, IDK if this is really even what OP meant, it's just where my mind went. If you put up with me to the end, thanks for reading.

MAN, can you imagine the clusterfuck of working at a company that’s become reliant on an AI layer between itself/its employees, and knowing how to do their jobs and use their systems and stuff? Like when that AI layer goes down, poof, you’re all hosed. And they don’t strike me as super robust…

I guess there are ways of training and running them locally, but they’re so seductive they’re definitely going to be deployed in places that aren’t up to the task of maintaining them in a sane state. Like… damn… cutting headcount in favor of relying on AI is like. A raccoon stuffing its head into a yogurt container. This is gonna be killing off organizations in a few years.

Unless AI gets good at destroying preexisting fucked up byzantine workflows and replacing them with simpler, human-friendly ones. That would be okay. But it is gonna irrevocably destroy a lot of records and botch a lot of database migrations on its way there.

#genai #long post #rambling and grumbling

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vertical-elevation · 2 years ago

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Why It’s a Mistake to Leave Talent Acquisition to HR?

Recruiting should never report to HR. As much as HR and people operations leaders may bristle at this declaration, it’s true: HR should never be responsible for managing recruitment strategies.

Before diving into the “why” behind this truth, let’s look at why so many companies feel compelled to make this common mistake. You definitely won’t hear recruiters say how much they love reporting to HR. The push always comes from the company itself. Here are three reasons for it.

IT’S THE WAY IT’S ALWAYS BEEN DONE

Companies get stuck in a rut of doing things, even if it doesn’t produce results. Sure, they may throw in a few new recruitment tech tools, but the reporting structure stays intact. When building a company, a CEO or founder may not be aware that there’s a real difference between HR and talent acquisition: The two are related to people, they reason, so why shouldn’t they be linked in the org chart? The fact that each department should separately report to the CEO may never come to mind.

IT’S A SHORTCUT

HR departments have their own protocols and do things by the book. In contrast, successful professional recruiters know that building a talent-centric organization — finding the right people, hiring them, and then putting them first — involves relationship-building, employee-centric strategies, and time. By pushing recruitment to HR, companies bypass these vital steps and take a shortsighted shortcut.

THEY WANT TO KEEP THINGS MOVING ALONG

It’s easy for companies to get into the mindset of churning through the hiring process. They see a chair waiting to be filled and their goal is to find a warm body to occupy that spot. That way, they can just keep things moving along. However, this never works out well.

THE CTO SOLUTION

So, what works better? The answer: Hire a chief talent officer who reports directly to the CEO.

Your CTO should be a highly trained executive recruiter or headhunter. This person will work to staff your company in partnership with the executive team and every manager down the line.

Talent strategy is the engine that will keep your company humming — which means your chief talent officer is just as critical as the other executives on your team. Consider these four facts:

CTO'S HAVE CRUCIAL RECRUITING EXPERIENCE

In my many years of research and working directly with human resources officers, 98% had zero experience in recruiting. And the other 2% had only a minimal amount of recruiting experience, either with recruitment agencies or corporate recruiting, but not enough to have mastered “the art of the search.”

To be a top recruiter, you must put in years of work and undergo extensive training. Even then, it’s not for the faint of heart. There are many setbacks. Hence, only 2% of all recruiters make it to the top. Recruiting isn’t something you can read a book about and just “get it.” It takes being on the ground with candidates, doing the searches, going back and forth, negotiating, knowing when and how to give offers, and more. Without that skill and knowledge, you won’t see lasting results.

RECRUITING REQUIRES RELATIONSHIP BUILDING

Quality recruiting takes years to master. You need a recruiter who’s not only experienced but also excels at long-term relationship building. Recruiters need emotional intelligence for the deep communication that’s required to get candidates to open up. Some candidates may be teetering between a “yes” and a “no” and need an authentically good listener to understand the hesitation. That requires years of understanding people and the human condition.

CTOS ARE NOT BOUND BY HR’S RED TAPE

Yes, budgets are important and red tape often exists for a reason, but too much concern for either can hinder the recruitment process. HR, by its very nature, isn’t inclined to color outside the lines. In many organizations, HR reports to the chief financial officer, which turns HR teams into bean-counters, not recruiters. Landing the right candidate may require workarounds HR won’t be likely to approve — or even think about — allowing great candidates to slip by.

GENERATE REVENUE WITH THE RIGHT RECRUIT

Although HR may appear to be moving things along and filling empty positions, a company won’t be building a talent-centric organization. Hiring people who aren’t the best fit fuels employee turnover; you’ll end up going through the hiring process all over again (and drain your budget in the process). When recruiting reports to HR, talent acquisition and recruitment become a cost center instead of the revenue generator it should be.

Choose the wrong team members, and you’ll end up with a revolving door of employees. So, why not give your talent recruitment and acquisition the autonomy it needs to succeed?

#business #growth #leadership #mindset #career #recruitment #hiring #coaching

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bizkonnect · 5 months ago

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Now, let’s talk about how tech companies org charts can help in this situation -

It goes beyond a static representation of a company hierarchy. These charts are dynamic and enriched with insights like decision-making roles, project priorities, and inter-departmental relationships. Ultimately, these actionable sales intelligence-driven insights not just helps you to understand who to contact, but what their pain points are and how they influence the buying process.

#tech company org charts #actionable org charts #actionable sales intelligence

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jcmarchi · 2 years ago

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Alper Tekin, Chief Product Officer at Findem – Interview Series

New Post has been published on https://thedigitalinsider.com/alper-tekin-chief-product-officer-at-findem-interview-series/

Alper Tekin, Chief Product Officer at Findem – Interview Series

Alper Tekin is Chief Product Officer at Findem an AI talent acquisition and management platform. Findem’s Talent Data Cloud is built upon the most advanced talent data. It learns as fast as the market moves to deliver unmatched talent intelligence to your entire team.

Previously you were a serial entrepreneur, acting as founder & CEO of several startups. What were some of the biggest hiring challenges that you encountered?

Hiring has been one of the most challenging aspects of my entrepreneurship journey. As entrepreneurs, we know people matter more than anything else and building the right team is the single most important job of any business leader. However, it is really tough to allocate the sufficient amount of time needed to find the right people when you’re maintaining so many other business activities involved in starting and scaling a company. Without objective data on who is available out there, it is hard to find the right set of people, and even harder to know if they will do well in your organization.

Could you share the vision for how Findem is building an autonomous talent platform for the HR team of the future?

Talent acquisition is a complex job with hundreds of tasks, done by tens of personas, across tens of point tools that do not talk to each other most of the time. Our vision is to remove this complexity through a combination of AI and workflow automation.

Our first and foremost goal is to support the talent teams by automating away mundane, repeatable and error-prone tasks from their day-to-day and assist people in making faster, better and more fair decisions with data. We’re already seeing use cases, such as a large tech company where they were using eight to 10 systems just to build a talent pipeline, and each was used in a siloed manner. It was taking them 80-100 clicks to accomplish a single task and now, with autonomous applications, they can perform the same task with one click.

Like nearly all business functions, talent organizations will undergo an AI-first transformation and our plan is to automate everything that can be automated, enabling recruiters and other talent professionals to reach their fullest potential. Autonomous applications will initially play a pivotal role in planning, pipeline and analytics, and then extend across the entire talent lifecycle, encompassing everything from workforce planning to talent pools to career development and succession planning.

Findem analyzes trillion of data points and takes advantage of what is called 3D data, could you clarify what 3D data is?

Findem ingests 1.6 trillion data points from hundreds of thousands of sources to generate entirely new talent data that doesn’t exist anywhere else and provides an understanding of an individual and the companies they’re associated with, over time. Findem uses these three dimensions of data – people and company data over time – to connect individual and company journeys and create enriched talent profiles.

Think of it this way: every person who’s worked in the modern job market has a journey and they leave behind a digital footprint. There are titles, job promotions, certificates, code contributions, publications, social posts and so forth. Similarly, companies have a journey. They have activities such as rounds of funding, IPOs and financial filings, as well as job descriptions, org charts, company reviews and leadership profiles – all of this data can chart an organization’s development and progress.

Traditionally, talent decisions have relied on a resume, job application and/or LinkedIn profile that only offer a one-dimensional slice of a person and company data. However, we’ve built a platform that’s capable of capturing thousands of data-points on people and company journeys and converting them into a massively enriched profile. The result is a more detailed and granular understanding of a person’s experience, skillset and impact than what was previously possible with manual research or from a user-generated LinkedIn profile.

With our Talent Data Cloud, entire careers are searchable on command through a GenAI interface. For example, you can ask the platform to show you CFOs at U.S. companies owned by PE firms who took a company from a negative to a positive operating margin or to give you a list of loyal product managers who worked for a B2B startup and saw it through a large Series C.

What are the different types of data points that are analyzed?

Our Talent Data Cloud dynamically and continuously leverages a language model to generate 3D data from hundreds of thousands of data sources.

It analyzes profile and contact data from the likes of LinkedIn, GitHub, StackOverflow, Kaggle, Dribble, Doximity, ResearchGate, WordPress and personal websites. Census data comes from the U.S. Census Bureau, of course. Additionally, we look at company data from funding announcements, IPO details, business models of over 8 million companies, and over 100,000 aggregated company and product categories. For verified skills, the platform analyzes over 300 million patents and publications, over 5 million open dataset and ML projects, and over 200 million open-source code repositories and other public contributions. And we importantly include ATS data that includes applicant profile information from the user’s ATS, which could be Greenhouse, Workday, SmartRecruiters, BambooHR, Lever and so on.

What is machine learning looking for when analyzing this data?

Findem is BI first, then uses AI to learn and make predictions based on factual data. We call this a deterministic model vs. a probabilistic model. For instance, we do not probabilistically infer that you have startup experience, we instead look at your employment history and see if any companies you work at have been classified as startups and then add a ‘startup experience’ attribute against your profile.

How is this data then transformed into attributes, and what are attributes?

Once data collection happens, we have an intelligence engine (think of it as a sophisticated SQL middleware) that can map data to any attribute we would like to create.

Attributes are the skills, experiences and characteristics of individuals and companies – and they’re both tangible and intangible. Tangible attributes include roles (current, past and role experiences), work experience, education, qualifications and other technical information. Intangible attributes can be far reaching, such as whether someone inspires loyalty, builds diverse teams or is mission driven.

Our attribute-based search enables HR teams to search for candidates across all channels in their talent ecosystem using practically any criteria you can think of.

How does the platform prevent gender or racial AI bias from creeping into hiring decisions?

Our platform was intentionally designed to not make decisions on behalf of any user, but rather for AI to assist the people in their decision-making. Using a BI-first strategy, the platform prioritizes the collection, analysis and presentation of data to provide insight and support for decision-making, then uses AI to learn, reason and make predictions or recommendations with trusted outcomes.

We’re a searching and matching platform, not a candidate evaluation platform, and AI is never used to make a subjective evaluation of a person. It never automatically advances or rejects applicants. Also, since Findem doesn’t use AI for searching and matching (these capabilities are BI based), it mitigates the risk of bias or discrimination creeping into the process.

How does Findem simplify the process of promoting internal staff?

At the core of it, we do not have to differentiate between ‘internal’ and ‘external’ talent. For any person in our database, our algorithm can find top-matching candidates whether they are outside or inside the organization.

What are all of the talent management tools that are offered?

We are consolidating top-of-funnel activities, so everything from talent sourcing to CRM to analytics. We also have a solution for internal mobility and we’re rolling out offerings for referral management and succession planning.

At what stage of the entrepreneurial journey should a startup be at before they reach out to Findem?

We service customers of all sizes, but our sweet spot tends to be companies that are in scaling mode with a few hundred employees.

Thank you for the great interview, readers who wish to learn more should visit Findem.

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theadmiringbog · 5 years ago

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I had a fragile but agreeable life: a job as an assistant at a small literary agency in Manhattan; a smattering of beloved friends on whom I exercised my social anxiety, primarily by avoiding them.

I wanted to make money, because I wanted to feel affirmed, confident, and valued. I wanted to be taken seriously. Mostly, I didn’t want anyone to worry about me.

Conversation with the cofounders had been so easy, and the interviews so much more like coffee dates than the formal, sweaty-blazer interrogations I had experienced elsewhere, that at a certain point I wondered if maybe the three of them just wanted to hang out.

They wore shirts that were always crisp and modestly buttoned to the clavicle. They were in long-term relationships with high-functioning women, women with great hair with whom they exercised and shared meals at restaurants that required reservations. They lived in one-bedroom apartments in downtown Manhattan and had no apparent need for psychotherapy. They shared a vision and a game plan. They weren’t ashamed to talk about it, weren’t ashamed to be openly ambitious. Fresh off impressive positions and prestigious summer internships at large tech corporations in the Bay Area, they spoke about their work like industry veterans, lifelong company men. They were generous with their unsolicited business advice, as though they hadn’t just worked someplace for a year or two but built storied careers. They were aspirational. I wanted, so much, to be like—and liked by—them.

It was thrilling to watch the moving parts of a business come together; to feel that I could contribute.

What I also did not understand at the time was that the founders had all hoped I would make my own job, without deliberate instruction. The mark of a hustler, a true entrepreneurial spirit, was creating the job that you wanted and making it look indispensable, even if it was institutionally unnecessary.

I wasn’t used to having the sort of professional license and latitude that the founders were given. I lacked their confidence, their entitlement. I did not know about startup maxims to experiment and “own” things. I had never heard the common tech incantation Ask forgiveness, not permission.

I had also been spoiled by the speed and open-mindedness of the tech industry, the optimism and sense of possibility. In publishing, no one I knew was ever celebrating a promotion. Nobody my age was excited about what might come next. Tech, by comparison, promised what so few industries or institutions could, at the time: a future.

“How would you explain the tool to your grandmother?” “How would you describe the internet to a medieval farmer?” asked the sales engineer, opening and closing the pearl snaps on his shirt,

Good interface design was like magic, or religion:

The first time I looked at a block of code and understood what was happening, I felt like nothing less than a genius.

Anything an app or website’s users did—tap a button, take a photograph, send a payment, swipe right, enter text—could be recorded in real time, stored, aggregated, and analyzed in those beautiful dashboards. Whenever I explained it to friends, I sounded like a podcast ad.

four-person companies trying to gamify human resources

... how rare the analytics startup was. Ninety-five percent of startups tanked. We weren’t just beating the odds; we were soaring past them.

While I usually spent sleepless nights staring at the ceiling and worrying about my loved ones’ mortality, he worked on programming side projects. Sometimes he just passed the time between midnight and noon playing a long-haul trucking simulator. It was calming, he said. There was a digital CB radio through which he could communicate with other players. I pictured him whispering into it in the dark.

At the start of each meeting, the operations manager distributed packets containing metrics and updates from across the company: sales numbers, new signups, deals closed. We were all privy to high-level details and minutiae, from the names and progress of job candidates to projected revenue. This panoramic view of the business meant individual contributions were noticeable; it felt good to identify and measure our impact.

Was this what it felt like to hurtle through the world in a state of pure confidence, I wondered, pressing my fingers to my temples—was this what it was like to be a man?

I was interested in talking about empathy, a buzzword used to the point of pure abstraction,

The hierarchy was pervasive at the analytics startup, ingrained in the CEO’s dismissal of marketing and insistence that a good product would sell itself.

He just taught himself to code over the summer, I heard myself say of a job candidate one afternoon. It floated out of my mouth with the awe of someone relaying a miracle.

As early employees, we were dangerous. We had experienced an early, more autonomous, unsustainable iteration of the company. We had known it before there were rules. We knew too much about how things worked, and harbored nostalgia and affection for the way things were.

The obsession with meritocracy had always been suspect at a prominent international company that was overwhelmingly white, male, and American, and had fewer than fifteen women in Engineering.

For years, my coworkers explained, the absence of an official org chart had given rise to a secondary, shadow org chart, determined by social relationships and proximity to the founders. Employees who were technically rank-and-file had executive-level power and leverage. Those with the ear of the CEO could influence hiring decisions, internal policies, and the reputational standing of their colleagues. “Flat structure, except for pay and responsibilities,” said an internal tools developer, rolling her eyes. “It’s probably easier to be a furry at this company than a woman.”

“It’s like no one even read ‘The Tyranny of Structurelessness,’” said an engineer who had recently read “The Tyranny of Structurelessness.”

Can’t get sexually harassed when you work remotely, we joked, though of course we were wrong.

I was in a million places at once. My mind pooled with strangers’ ideas, each joke or observation or damning polemic as distracting and ephemeral as the next. It wasn’t just me. Everyone I knew was stuck in a feedback loop with themselves. Technology companies stood by, ready to become everyone’s library, memory, personality. I read whatever the other nodes in my social networks were reading. I listened to whatever music the algorithm told me to. Wherever I traveled on the internet, I saw my own data reflected back at me: if a jade face-roller stalked me from news site to news site, I was reminded of my red skin and passive vanity. If the personalized playlists were full of sad singer-songwriters, I could only blame myself for getting the algorithm depressed.

As we left the theater in pursuit of a hamburger, I felt rising frustration and resentment. I was frustrated because I felt stuck, and I was resentful because I was stuck in an industry that was chipping away at so many things I cared about. I did not want to be an ingrate, but I had trouble seeing why writing support emails for a venture-funded startup should offer more economic stability and reward than creative work or civic contributions. None of this was new information—and it was not as if tech had disrupted a golden age of well-compensated artists—but I felt it fresh.

I had never really considered myself someone with a lifestyle, but of course I was, and insofar as I was aware of one now, I liked it. The tech industry was making me a perfect consumer of the world it was creating. It wasn’t just about leisure, the easy access to nice food and private transportation and abundant personal entertainment. It was the work culture, too: what Silicon Valley got right, how it felt to be there. The energy of being surrounded by people who so easily articulated, and satisfied, their desires. The feeling that everything was just within reach.

We wanted to be on the side of human rights, free speech and free expression, creativity and equality. At the same time, it was an international platform, and who among us could have articulated a coherent stance on international human rights? We sat in our apartments tapping on laptops purchased from a consumer-hardware company that touted workplace tenets of diversity and liberalism but manufactured its products in exploitative Chinese factories using copper and cobalt mined in Congo by children. We were all from North America. We were all white, and in our twenties and thirties. These were not individual moral failings, but they didn’t help. We were aware we had blind spots. They were still blind spots. We struggled to draw the lines. We tried to distinguish between a political act and a political view; between praise of violent people and praise of violence; between commentary and intention. We tried to decipher trolls’ tactical irony. We made mistakes.

I did not want two Silicon Valleys. I was starting to think the one we already had was doing enough damage. Or, maybe I did want two, but only if the second one was completely different, an evil twin: Matriarchal Silicon Valley. Separatist-feminist Silicon Valley. Small-scale, well-researched, slow-motion, regulated Silicon Valley—men could hold leadership roles in that one, but only if they never used the word “blitzscale” or referred to business as war.

“Progress is so unusual and so rare, and we’re all out hunting, trying to find El Dorado,” Patrick said.

“Almost everyone’s going to return empty-handed. Sober, responsible adults aren’t going to quit their jobs and lives to build companies that, in the end, may not even be worth it. It requires, in a visceral way, a sort of self-sacrificing.”

Only later did I consider that he might have been trying to tell me something.

Abuses were considered edge cases, on the margin—flaws that could be corrected by spam filters, or content moderators, or self-regulation by unpaid community members. No one wanted to admit that abuses were structurally inevitable: indicators that the systems—optimized for stickiness and amplification, endless engagement—were not only healthy, but working exactly as designed.

The SF Bay Area is like Rome or Athens in antiquity, posted a VC. Send your best scholars, learn from the masters and meet the other most eminent people in your generation, and then return home with the knowledge and networks you need. Did they know people could see them?

I couldn’t imagine making millions of dollars every year, then choosing to spend my time stirring shit on social media. There was almost a pathos to their internet addiction. Log off, I thought. Just email each other.

All these people, spending their twenties and thirties in open-plan offices on the campuses of the decade’s most valuable public companies, pouring themselves bowls of free cereal from human bird feeders, crushing empty cans of fruit-tinged water, bored out of their minds but unable to walk away from the direct deposits—it was so unimaginative. There was so much potential in Silicon Valley, and so much of it just pooled around ad tech, the spillway of the internet economy.

Though I did not want what Patrick and his friends wanted, there was still something appealing to me about the lives they had chosen. I envied their focus, their commitment, their ability to know what they wanted, and to say it out loud—the same things I always envied.

I wanted to believe that as generations turned over, those coming into economic and political power would build a different, better, more expansive world, and not just for people like themselves. Later, I would mourn these conceits. Not only because this version of the future was constitutionally impossible—such arbitrary and unaccountable power was, after all, the problem—but also because I was repeating myself. I was looking for stories; I should have seen a system. The young men of Silicon Valley were doing fine. They loved their industry, loved their work, loved solving problems. They had no qualms. They were builders by nature, or so they believed. They saw markets in everything, and only opportunities. They had inexorable faith in their own ideas and their own potential. They were ecstatic about the future. They had power, wealth, and control. The person with the yearning was me.

could have stayed in my job forever, which was how I knew it was time to go. The money and the ease of the lifestyle weren’t enough to mitigate the emotional drag of the work: the burnout, the repetition, the intermittent toxicity. The days did not feel distinct. I felt a widening emptiness, rattling around my studio every morning, rotating in my desk chair. I had the luxury, if not the courage, to do something about it.

As I stood in the guest entrance, waiting for the stock plan administrator to collect the paperwork, I watched my former coworkers chatting happily with one another in the on-site coffee shop and felt, wrenchingly, that leaving had been a huge mistake. Certain unflattering truths: I had felt unassailable behind the walls of power. Society was shifting, and I felt safer inside the empire, inside the machine. It was preferable to be on the side that did the watching than on the side being watched.

#uncanny valley #books #best of

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sarasa-cat · 6 years ago

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@auronlu :

I hear what you are saying. There is an strong element of fear and desperation that cuts across your examples and mine.

I’m going to add a little more context to the sexism side, focusing only on women in tech for just a moment (because the story there is very obvious)

A well known disturbing trend in STEM that researchers have been following since (at least) the 90s (and probably earlier) is how the percentage of women graduating with Computer Science degrees peaked in the 80s and has since declined. Now the numbers are on par with the rocking backwardness of the 70s. >_<. (See note 1)

Yet this is completely opposite of the trend of women seeking higher education — that percentage has grown steadily over the past few generations.

And it certainly has not happened because women think computer science is too hard or because it has become more difficult. But what has happened is that a computer science degree commands a far higher salary post-graduation than it did a few decades ago. A few decades ago, a typical starting comp sci (software programmer / software engineer) salary was a standard middle class white collar job. Salaries started pushing into upper middle class in the very late 90s (look at what happens in the chart up above), and by the mid-00s, a 22 year old with a freshly minted CS degree could walk into a job in Silicon Valley that dropped over a quarter million dollars per year (salary + annual bonus + stock) into their bank account. That’s a far cry from $30k + health insurance in the early-mid 90s. AND, there has been a lot of research performed in multiple nations — multiple kinds of cultures — showing that when more women enter a field, salaries trend down (*cough* sexism) and when a field is highly competative and highly male dominated, and profit oriented, salaries are more likely to push up. Thus, it isn’t surprising that women have felt less and less welcome or have been actively PUSHED OUT THE DOOR in a field where, today, a young 20-something who is smart enough to get hired by the right company can literally expect to make a few million before their 30th b-day.

If you google today’s news for a story “Microsoft Staff are openly questioning the value of diversity” (sorry - on ipad and it is difficult for me to add multiple links -_-), you’ll see how ugly this is getting — it is really just a repeat of what has happened at Google, Uber, and other tech companies over the past couple of years but the shit people are saying on internal mailings lists that has been leaked to the media is very blatant. Certain people (men and women, yes, women, sadly) certainly held some of these thoughts 10 and 20 years ago — because I have heard the words come out of their mouths — but the escallation all the way up to the chain — up to senior management at these companies and into the media and onto twitter and blogs — is very different and far more organized as a mode of attack.

So, when you say fear of loss of pre-existing privileges, including fast-tracking into the inner circle because they are ‘the right kind of culture fit’ (male, white) and, thus, fast-tracking into a sense of career success and into financial power, I certainly agree that FEAR is an issue but .... why does it keep getting worse, especially when the comp sci pipeline has been producing less and less women since the early 80s. I mean, think about it. That pipeline hit its peek in 1983/84. Those women are pushing 60 now — late career! The men in my generation and the generation after have LESS COMPETITION from women. Women are practically invisible in the engineering side of most high-paying tech company org charts. So ... fear of women taking over isn’t really supported by the data.

Thus, what are they afraid of?! And this is where I wonder if massive economic changes between how things are now vs how things were in the 1950s early 1980s tells the other half of the story. Gen X in the western world was the first 20th/21st century generation to realize that they weren’t going to do as well as their parents and as Gen X ages, data shows that they are defintely feeling fucked, especially in the US. But, Gen X were just the test subjects, so to speak.** Millenials and the oldest Gen Zs are even more economically screwed, which is absolutely depressing to think about based on how screwed Gen X is in comparison to those before them.

(** Also, pretty much anyone of any age is in an economic nightmare in the US thanks to spiraling healthcare costs, which definitely affects the older generations who, on average did far better in their youth, especially if they were white.)

So ... is the fact that being middle class now means that you are one car accident or hospital visit away from potential poverty, and student debt/education cost has consistently risen since the late 80s/early90s, and the insane cost of health care in the US (omfg), etc. All of this. Is this what has really pushd the fear into attack mode? Because merely saying “women are flooding this field so we men have to protect our access to good salaries and push back” is entirely NOT the case in computer science and software engineering.

(1) http://www.aei.org/publication/chart-of-the-day-the-declining-female-share-of-computer-science-degrees-from-28-to-18/

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orgkonnectt · 29 days ago

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offlinerenta · 3 years ago

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