Data Science

Weekly Entering & Transitioning - Thread 23 Jan, 2023 - 30 Jan, 2023

2023-01-23T05:01:09+00:00

Welcome to this week's entering & transitioning thread! This thread is for any questions about getting started, studying, or transitioning into the data science field. Topics include:

Learning resources (e.g. books, tutorials, videos)
Traditional education (e.g. schools, degrees, electives)
Alternative education (e.g. online courses, bootcamps)
Job search questions (e.g. resumes, applying, career prospects)
Elementary questions (e.g. where to start, what next)

While you wait for answers from the community, check out the FAQ and Resources pages on our wiki. You can also search for answers in past weekly threads.

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[Official] 2022 End of Year Salary Sharing thread

2023-01-02T17:18:29+00:00

See last year's Salary Sharing thread here.

MODNOTE: Originally borrowed this from r/cscareerquestions. Some people like these kinds of threads, some people hate them. If you hate them, that's fine, but please don't get in the way of the people who find them useful. Thanks!

This is the official thread for sharing your current salaries (or recent offers).

Please only post salaries/offers if you're including hard numbers, but feel free to use a throwaway account if you're concerned about anonymity. You can also generalize some of your answers (e.g. "Large biotech company"), or add fields if you feel something is particularly relevant.

Title:
Tenure length:
Location:
- $Remote:
Salary:
Company/Industry:
Education:
Prior Experience:
- $Internship
- $Coop
Relocation/Signing Bonus:
Stock and/or recurring bonuses:
Total comp:

Note that while the primary purpose of these threads is obviously to share compensation info, discussion is also encouraged.

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As a hiring manager - this, this right here

2023-01-27T14:48:21+00:00

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Does this field attract arrogant people?

2023-01-27T15:00:46+00:00

I have just noticed that a lot of people in this profession are incredibly rude, holier than thou, and arrogant. This subreddit is a big offender of it too. I spend a lot of time working with and hiring junior data scientists and the way they get treated by more senior people is just appalling. I don’t care if a new hire doesn’t get all the concepts in their first year. I REALLY care if my senior hire is treating them poorly for having a lot of questions.

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Does anyone else worry they’ll forget all their maths/fundamentals?

2023-01-27T19:52:36+00:00

I’m a Data Analyst with a Physics background. Throughout my degree I really enjoyed deriving equations, and fully understanding where things came from - pen and paper style. I liked learning and knowing the fundamentals.

When I was doing my PhD and applying statistical techniques, I similarly liked trying to calculate certain things myself by hand in simple scenarios (such as a t-test with not too many data points), rather than always using pre-programmed functions. I had a lot more time to dedicate to learning and checking things made sense.

In my work now as an analyst, whilst I get L&D time, I just don’t have the time anymore to thoroughly check over things and make sure they make sense. I just have to trust that the R functions are doing what I expect. I have to trust that my chi-squared test is doing what I think, for example. Or that my logistic regression is producing a reasonable output.

I can’t remember the last time I got a pen and paper out and had to differentiate or integrate something, or multiply matrices, or work out the Fourier transform of a function.

Maybe I’m just being nostalgic and having imposter syndrome but it feels like I’m slowly forgetting all of that underlying maths, and that I’ll end up at the point where I can do basic statistical programming and nothing more.

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SQL for beginners

2023-01-28T10:14:35+00:00

What resources would you recommend for learning SQL

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In the R vs. Python discussion, where do you stand?

2023-01-28T12:34:25+00:00

I am new to data science, and I have been following a lot of people in the field on LI. It seems like there is a discussion around what's the better language for DS. Some people argue R is better, some python. I know a little of each with my focus being on python (I am not an expert programmer though). What are your opinions? Any idea where the field is heading? Should I focus on R more?

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Implementing GPTZero from scratch

2023-01-28T12:08:28+00:00

We've gone through the original implementation of GPTZero and successfully reverse engineer it. (it gives the same results as original GPTZero). We've also recorded the implementation process which can be found below.

Youtube Implementation Video: https://youtu.be/x9H-aY5sCDA
Github: https://github.com/BurhanUlTayyab/GPTZero
Website: https://gptzero.sg
Discord: https://discord.com/invite/F3kFan28vH

We're also working on a GPTZerov2 (inspired by LLM based transformers and GANs), which would be more accurate, and can detect lines changed by humans.

Please give some feedback on our work.

Thanks

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Suggestions on courses that can be completed in 6 months, just for KPI sake

2023-01-28T11:57:06+00:00

With the new management, HR has mandated to submit at least a certificate, Half yearly.

The only rule is it should not be given for participation for the ones like udemy, but at least a test of sorts is taken to provide the certificate.

Me and my team have experience working with a lot of tools, but lack any professional certification

My colleagues have listed the below certifications

Python ,R
AWS/GCP/Azure

suggest any courses you know of, Thanks in advance.

Also, Any tips in handling these discussions with HR, they are introducing and bringing in a lot of things on top of existing KPIs. if you know any reddit for HR related, point me there.

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Anyone that have been working with causal models?

2023-01-28T05:52:08+00:00

I've used causal models to estimate average treatment effects for various questions. But I have not yet implemented any large scale model for individual treatment effects.

There seems to be few people with experience and I have not found a good community for asking questions. So I thought I check out if we have people with experience here.

Edit: I am especially interested if there are anyone that have been moving from standard classification/regression models to causal modeling.

Edit2: I am not that interested in learning models or different techniques. I know what to read and where to look. I am looking for people who have implemented such models at large scale in a commercial setting. Some questions:

How was it going from classification models to causal models?
What results did your organization observe?
Have you been working with "scenario planning". Optimizing for revenue vs retention and similar.
What type of models and problem did you work with?
Have you been implementing it in a larger system, such as a decision hub?

And so forth.

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What are the major math/stats concepts do you need to master to be a good data scientist?

2023-01-28T02:56:39+00:00

I am just joining the field and I am a little terrified to be honest. I think imposter syndrome takes over pretty quickly for me, but what might help me is digging deeper into, and mastering, math/stats concepts that are critical to any data scientist. What are the most important ones IYO?

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How to predict a series of events

2023-01-28T11:32:57+00:00

Context: I work for an e-commerce that is trying to come up with the best series of marketing actions to engage customer

Basically we want to decide if it’s better to send 5 e-mails or 3 e-mails + 2 SMSs according to the propensity of a customer buying something

Does anyone have any ideia how to tackle this?

I was thinking about a uplift model for each action and create something based on that

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Do ALL data science jobs use machine learning? About what percentage of them do not use it?

2023-01-28T00:39:27+00:00

Do ALL data science jobs use machine learning? About what percentage of them do not use it?

Just wondering, because although I have taught myself many data science concepts, my machine learning is a little thin.

Thanks for your help!

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I'm a tired of interviewing fresh graduates that don't know fundamentals.

2023-01-26T23:58:48+00:00

So I have just wrapped up first round interviews for an open position on my team. I work in banking and most of my career involves building regression or logistic regression models.

One of the trends I've seen since the tech data science boom started is that there just seems to be a drop in the technical level for peoples with masters degree on fundamentals. It seems too many candidates with masters degrees do not understand mathematical assumptions of most of the models they are using even at a conceptual level. For example, during the interview I asked most candidates about regression and what assumptions are required.

Nearly every single masters level candidate didn't know why the specific assumptions were made (even if they could correctly list them), could not answer questions on what happens when you violate an assumption, and did not know how to test violation of those assumptions or how to address those issues. Whats disconcerting is these are candidates coming out of professional masters programs from the worlds leading universities and most of them will end up in jobs where modeling error can have multi-million dollar impacts.

For some additional context: The comment here is explicitly here about standard of candidates I interviewed for people with masters degrees. Most of the Ph.D jobs met standards we expect, even though the job does not require one. The job is one that is very specifically related to regression modeling, time series.

Some clarification: This isn't not having trouble finding candidates post. This is a role at a industry leading firm, and there is no shortage of good candidates. What I am specifically addressing in this posts is that candidates we are interviewing with masters degrees don't know text book stuff they should know based on whats listed in their resume.

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Data science conferences / meetings in UK

2023-01-28T07:26:24+00:00

Does anyone know of any interesting conferenes or meet ups on data science and machine learning in London, not only where papers are presented, but also where companies share their expertise, kaggle winners talk about their solutions, etc?

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Is this sub only for new grads, students, and career swaps?

2023-01-27T07:28:29+00:00

As the title mentions, I’ve been in this sub for around 5 months. Hoping to see more in-depth data science discussion rather than students trying to justify that data science is worth going into or how to break into data science from another career. I don’t think I’ve seen another sub quite like this one with the degree of questions asked like this from people outside of the field. Especially how many duplicate/repeat questions I see every day.

Are there better sub reddits to be in besides this where more advanced topics are discussed?

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Others presenting my work

2023-01-27T19:44:16+00:00

Looking for advice on how to handle this.

I build a dashboard or report or automated tool and showcase it to data colleagues in other departments for feedback and adoption. Next thing I know, an executive is driving an all-employees mtg and praising someone else for my work because they showed it to that exec. That other person gets invited to leadership mtgs, then promoted. This has happened several times in two different companies.

Am I blowing this out of proportion? Should I stop collaborating? Is this common or am I just unlucky?

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What programming languages?

2023-01-28T06:16:52+00:00

I’m in my first year of Uni studying Data Science. I’ve learned a bunch of Python on my own and am being taught Java. Also have some minor understanding of C# and C. What programming languages and tools should I focus on if I want to get an actual job? I’ve seen a lot of mentions of JavaScript and React. Are those core components of a hire-able CV?

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Is it worth to spend time in training and getting certified in UiPath as a Data Scientist?

2023-01-28T06:10:10+00:00

I have 6+ years of experience as a Data Scientist. My current organization is asking us to get certified in UiPath (optional). Is it worth to spend time in learning a RPA tool? Is it useful for in AIML projects?

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Opinion on Current Experience?

2023-01-28T04:39:16+00:00

Hello everyone! Just for a bit of context, I am 23 years old and just started my Data Analyst career. I have not graduated with my bachelors yet but I believe over the summer I should be able to finish core req classes and should be golden.

I currently have experience working with R, Tableau and Tableau Prep. I have been fortunate enough to work at a start up company and really gain a lot of exposure to other tools such as Heap and Salesforce. So far I have 8 months of experience.

During these 8 months I have completed a series of deep analysis of the company products and its customers. Worked from analyzing customer behavior, after cleaning really messy historical data, to creating customer segments based on the key performance metrics I also had created. Using Heap, I had to define UI code of our plattforms to then make analysis of user interactions. Report those findings and suggest areas of improvement.

I feel confident in cleaning data but, as its notoriously known, I do take my time because I don't like finding later mid-analysis that the data is off. Whenever I find myself repeatedly doing something on excel, I create executable R scripts to speed the process. I often do this when uploading data into Salesforce.

I plan to read some amazing resources I found for ML and statistics and apply some concepts into the company. I found that this start up in particular is always open for new ideas and projects.

My current goal is to shoot for $80k salary once I graduate, how realistic are these goals? I just dont know how much my experience is valued at the current market.

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A python module to generate optimized prompts, Prompt-engineering & solve different NLP problems using GPT-n (GPT-3, ChatGPT) based models and return structured python object for easy parsing

2023-01-27T19:53:34+00:00

Hi folks,

I was working on a personal experimental project related to GPT-3, which I thought of making it open source now. It saves much time while working with LLMs.

If you are an industrial researcher or application developer, you probably have worked with GPT-3 apis. A common challenge when utilizing LLMs such as #GPT-3 and BLOOM is their tendency to produce uncontrollable & unstructured outputs, making it difficult to use them for various NLP tasks and applications.

To address this, we developed Promptify, a library that allows for the use of LLMs to solve NLP problems, including Named Entity Recognition, Binary Classification, Multi-Label Classification, and Question-Answering and return a python object for easy parsing to construct additional applications on top of GPT-n based models.

Features 🚀

🧙‍♀️ NLP Tasks (NER, Binary Text Classification, Multi-Label Classification etc.) in 2 lines of code with no training data required
🔨 Easily add one-shot, two-shot, or few-shot examples to the prompt
✌ Output is always provided as a Python object (e.g. list, dictionary) for easy parsing and filtering
💥 Custom examples and samples can be easily added to the prompt
💰 Optimized prompts to reduce OpenAI token costs

GITHUB: https://github.com/promptslab/Promptify
Examples: https://github.com/promptslab/Promptify/tree/main/examples
For quick demo -> Colab

Try out and share your feedback. Thanks :)

NER Examples

https://preview.redd.it/5o5uqk3k2nea1.png?width=1398&format=png&auto=webp&s=96c25820a698a83dfeb0e8f7f37682d9d27c06cb

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Resources (books) for DA/interview

2023-01-28T00:07:21+00:00

Hi All, I was looking for some books for DA interview. Does it even exist? All I see is for DS interviews?

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What my next career move - Currently Senior HRIS Analyst.

2023-01-28T03:00:28+00:00

I’m not sure where to go next. I’ve been in HRIS for 7 years now (senior for 3). It doesn’t seem like leadership is going anywhere anytime soon and I’m not sure what my next move should be. I wear many hats in my role but the things I enjoy most are the data analytics, metric creation and project implementation. I don’t know any SQL so I’m worried I’d have to take a paycut to move out of the HR world. Anyone else with similar career pathing?

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Invited to On-Site Interview after "Final" Round

2023-01-27T17:19:19+00:00

I'm relatively new to data science and I've been interviewing with a company for about a month. I've been through a coding test, case study, multiple rounds with members of the team, and what I was told would be a final interview with the head of their team.

A few days later, I received an email saying they'd like to fly me out to their offices. And my travel date would be ~2.5 weeks into the future (which is surprisingly far out, I thought).

Has anyone else been asked on site after a final-round interview? I think that they're either interested in me or buying time while another candidate considers their offer. Thoughts?

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⭕ What People Are Missing About Microsoft’s $10B Investment In OpenAI

2023-01-27T10:52:18+00:00

Sam Altman Might Have Just Pulled Off The Coup Of The Decade

Microsoft is investing $10B into OpenAI!

There is lots of frustration in the community about OpenAI not being all that open anymore. They appear to abandon their ethos of developing AI for everyone, free of economic pressures.

The fear is that OpenAI’s models are going to become fancy MS Office plugins. Gone would be the days of open research and innovation.

However, the specifics of the deal tell a different story.

To understand what is going on, we need to peek behind the curtain of the tough business of machine learning. We will find that Sam Altman might have just orchestrated the coup of the decade!

To appreciate better why there is some three-dimensional chess going on, let’s first look at Sam Altman’s backstory.

Let’s go!

A Stellar Rise

Back in 2005, Sam Altman founded Loopt and was part of the first-ever YC batch. He raised a total of $30M in funding, but the company failed to gain traction. Seven years into the business Loopt was basically dead in the water and had to be shut down.

Instead of caving, he managed to sell his startup for $43M to the finTech company Green Dot. Investors got their money back and he personally made $5M from the sale.

By YC standards, this was a pretty unimpressive outcome.

However, people took note that the fire between his ears was burning hotter than that of most people. So hot in fact that Paul Graham included him in his 2009 essay about the five founders who influenced him the most.

He listed young Sam Altman next to Steve Jobs, Larry & Sergey from Google, and Paul Buchheit (creator of GMail and AdSense). He went on to describe him as a strategic mastermind whose sheer force of will was going to get him whatever he wanted.

And Sam Altman played his hand well!

He parleyed his new connections into raising $21M from Peter Thiel and others to start investing. Within four years he 10x-ed the money [2]. In addition, Paul Graham made him his successor as president of YC in 2014.

Within one decade of selling his first startup for $5M, he grew his net worth to a mind-bending $250M and rose to the circle of the most influential people in Silicon Valley.

Today, he is the CEO of OpenAI — one of the most exciting and impactful organizations in all of tech.

However, OpenAI — the rocket ship of AI innovation — is in dire straights.

OpenAI is Bleeding Cash

Back in 2015, OpenAI was kickstarted with $1B in donations from famous donors such as Elon Musk.

That money is long gone.

In 2022 OpenAI is projecting a revenue of $36M. At the same time, they spent roughly $544M. Hence the company has lost >$500M over the last year alone.

This is probably not an outlier year. OpenAI is headquartered in San Francisco and has a stable of 375 employees of mostly machine learning rockstars. Hence, salaries alone probably come out to be roughly $200M p.a.

In addition to high salaries their compute costs are stupendous. Considering it cost them $4.6M to train GPT3 once, it is likely that their cloud bill is in a very healthy nine-figure range as well [4].

So, where does this leave them today?

Before the Microsoft investment of $10B, OpenAI had received a total of $4B over its lifetime. With $4B in funding, a burn rate of $0.5B, and eight years of company history it doesn’t take a genius to figure out that they are running low on cash.

It would be reasonable to think: OpenAI is sitting on ChatGPT and other great models. Can’t they just lease them and make a killing?

Yes and no. OpenAI is projecting a revenue of $1B for 2024. However, it is unlikely that they could pull this off without significantly increasing their costs as well.

Here are some reasons why!

The Tough Business Of Machine Learning

Machine learning companies are distinct from regular software companies. On the outside they look and feel similar: people are creating products using code, but on the inside things can be very different.

To start off, machine learning companies are usually way less profitable. Their gross margins land in the 50%-60% range, much lower than those of SaaS businesses, which can be as high as 80% [7].

On the one hand, the massive compute requirements and thorny data management problems drive up costs.

On the other hand, the work itself can sometimes resemble consulting more than it resembles software engineering. Everyone who has worked in the field knows that training models requires deep domain knowledge and loads of manual work on data.

To illustrate the latter point, imagine the unspeakable complexity of performing content moderation on ChatGPT’s outputs. If OpenAI scales the usage of GPT in production, they will need large teams of moderators to filter and label hate speech, slurs, tutorials on killing people, you name it.

Alright, alright, alright! Machine learning is hard.

OpenAI already has ChatGPT working. That’s gotta be worth something?

Foundation Models Might Become Commodities:

In order to monetize GPT or any of their other models, OpenAI can go two different routes.

First, they could pick one or more verticals and sell directly to consumers. They could for example become the ultimate copywriting tool and blow Jasper or copy.ai out of the water.

This is not going to happen. Reasons for it include:

To support their mission of building competitive foundational AI tools, and their huge(!) burn rate, they would need to capture one or more very large verticals.
They fundamentally need to re-brand themselves and diverge from their original mission. This would likely scare most of the talent away.
They would need to build out sales and marketing teams. Such a step would fundamentally change their culture and would inevitably dilute their focus on research.

The second option OpenAI has is to keep doing what they are doing and monetize access to their models via API. Introducing a pro version of ChatGPT is a step in this direction.

This approach has its own challenges. Models like GPT do have a defensible moat. They are just large transformer models trained on very large open-source datasets.

As an example, last week Andrej Karpathy released a video of him coding up a version of GPT in an afternoon. Nothing could stop e.g. Google, StabilityAI, or HuggingFace from open-sourcing their own GPT.

As a result GPT inference would become a common good. This would melt OpenAI’s profits down to a tiny bit of nothing.

In this scenario, they would also have a very hard time leveraging their branding to generate returns. Since companies that integrate with OpenAI’s API control the interface to the customer, they would likely end up capturing all of the value.

An argument can be made that this is a general problem of foundation models. Their high fixed costs and lack of differentiation could end up making them akin to the steel industry.

To sum it up:

They don’t have a way to sustainably monetize their models.
They do not want and probably should not build up internal sales and marketing teams to capture verticals
They need a lot of money to keep funding their research without getting bogged down by details of specific product development

So, what should they do?

The Microsoft Deal

OpenAI and Microsoft announced the extension of their partnership with a $10B investment, on Monday.

At this point, Microsoft will have invested a total of $13B in OpenAI. Moreover, new VCs are in on the deal by buying up shares of employees that want to take some chips off the table.

However, the astounding size is not the only extraordinary thing about this deal.

First off, the ownership will be split across three groups. Microsoft will hold 49%, VCs another 49%, and the OpenAI foundation will control the remaining 2% of shares.

If OpenAI starts making money, the profits are distributed differently across four stages:

First, early investors (probably Khosla Ventures and Reid Hoffman’s foundation) get their money back with interest.
After that Microsoft is entitled to 75% of profits until the $13B of funding is repaid
When the initial funding is repaid, Microsoft and the remaining VCs each get 49% of profits. This continues until another $92B and $150B are paid out to Microsoft and the VCs, respectively.
Once the aforementioned money is paid to investors, 100% of shares return to the foundation, which regains total control over the company. [3]

What This Means

This is absolutely crazy!

OpenAI managed to solve all of its problems at once. They raised a boatload of money and have access to all the compute they need.

On top of that, they solved their distribution problem. They now have access to Microsoft’s sales teams and their models will be integrated into MS Office products.

Microsoft also benefits heavily. They can play at the forefront AI, brush up their tools, and have OpenAI as an exclusive partner to further compete in a bitter cloud war against AWS.

The synergies do not stop there.

OpenAI as well as GitHub (aubsidiary of Microsoft) e. g. will likely benefit heavily from the partnership as they continue to develop GitHub Copilot.

The deal creates a beautiful win-win situation, but that is not even the best part.

Sam Altman and his team at OpenAI essentially managed to place a giant hedge. If OpenAI does not manage to create anything meaningful or we enter a new AI winter, Microsoft will have paid for the party.

However, if OpenAI creates something in the direction of AGI — whatever that looks like — the value of it will likely be huge.

In that case, OpenAI will quickly repay the dept to Microsoft and the foundation will control 100% of whatever was created.

Wow!

Whether you agree with the path OpenAI has chosen or would have preferred them to stay donation-based, you have to give it to them.

This deal is an absolute power move!

I look forward to the future. Such exciting times to be alive!

As always, I really enjoyed making this for you and I sincerely hope you found it useful!

Thank you for reading!

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References:

[1] https://golden.com/wiki/Sam_Altman-J5GKK5

[2] https://www.newyorker.com/magazine/2016/10/10/sam-altmans-manifest-destiny

[3] Article in Fortune magazine

[4] https://arxiv.org/abs/2104.04473 Megatron NLG

[5] https://www.crunchbase.com/organization/openai/company_financials

[6] Elon Musk donation https://www.inverse.com/article/52701-openai-documents-elon-musk-donation-a-i-research

[7] https://a16z.com/2020/02/16/the-new-business-of-ai-and-how-its-different-from-traditional-software-2/

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Tell me the coolest project/research you’ve worked on in your career, and the outcome of said project.

2023-01-27T19:15:49+00:00

College student here. Just finished a meeting with a professor at my uni who seemed multitudes more educated and intelligent then I am in the current state of my “career”. We chatted back and forth for around an hour about techniques and avenues of approach given different starting disciplines (stats vs computer science, etc).

The book we’re reading in class, that I doubt anyone besides me has opened since so far it’s very slow, has some of his published works and data used for a really cool project he did with his PHD. To refrain from giving up my anonymity I’m not going to disclose what it was but I was like damn after the convo.

I want to hear all the coolest shit you guys out there have done, ranging from research avenues, to professional settings, to whatever stuff you cooked up on your own time.

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Where to go outside of tech?

2023-01-27T22:36:25+00:00

Hey folks,

I'm a senior data scientist with 5+ years of experience mostly on marketing and product. I've spent my whole career in startups and tech and am honestly just tired of it at this point.

Nothing is stable, priorities, technology, and competition are constantly changing. Moreover, although you see all these tiktok tech influencers showcase their cushy jobs, I have always found that tech is extremely demanding on your mental energy. This has only worsened for me since covid as wfh also means you always have to be on and available even in my own home.

Is this what it's like across other industries as well? Are there any chill jobs where I don't always have to be on my A-game?

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Data Science

Weekly Entering & Transitioning - Thread 23 Jan, 2023 - 30 Jan, 2023

[Official] 2022 End of Year Salary Sharing thread

As a hiring manager - this, this right here

Does this field attract arrogant people?

Does anyone else worry they’ll forget all their maths/fundamentals?

SQL for beginners

In the R vs. Python discussion, where do you stand?

Implementing GPTZero from scratch

Suggestions on courses that can be completed in 6 months, just for KPI sake

Anyone that have been working with causal models?

What are the major math/stats concepts do you need to master to be a good data scientist?

How to predict a series of events

Do ALL data science jobs use machine learning? About what percentage of them do not use it?

I'm a tired of interviewing fresh graduates that don't know fundamentals.

Data science conferences / meetings in UK

Is this sub only for new grads, students, and career swaps?

Others presenting my work

What programming languages?

Is it worth to spend time in training and getting certified in UiPath as a Data Scientist?

Opinion on Current Experience?

A python module to generate optimized prompts, Prompt-engineering & solve different NLP problems using GPT-n (GPT-3, ChatGPT) based models and return structured python object for easy parsing

Resources (books) for DA/interview

What my next career move - Currently Senior HRIS Analyst.

Invited to On-Site Interview *after* "Final" Round

⭕ What People Are Missing About Microsoft’s $10B Investment In OpenAI

A Stellar Rise

OpenAI is Bleeding Cash

The Tough Business Of Machine Learning

Foundation Models Might Become Commodities:

The Microsoft Deal

What This Means

Tell me the coolest project/research you’ve worked on in your career, and the outcome of said project.

Where to go outside of tech?

Invited to On-Site Interview after "Final" Round