u/WhatsTheImpactdotcom

For senior and staff+ level data science interviews, there are subtle ways you can showcase your technical skillset and stand out among the competition. First, most interviews have a clear rubric: You absolutely need to clear this rubric first, which might involve recognizing SUTVA violations, designing a switchback or geo-based experiment, performing a power analysis, dealing with peeking, or choosing between observational causal inference models.

If you jump ahead to more complex modeling without demonstrating the fundamentals being tested, it can be a quick path to failure. However, in those last 5 minutes or so of an interview, there are opportunities to shine.

For example, if you're discussing p-values for synthetic controls, you should probably mention permutation tests. But if you're aware of alternative approaches and under which contexts to use these alternatives, then these last few minutes are your time to describe why.

In an applied modeling round, you will likely get asked about class imbalance. You should probably name drop SMOTE as foundational; but if you're familiar with alternatives--and *why*--this is your time to shine.

If you're being asked about alpha thresholds, you should probably name drop "classical statistical significance," but if you can discuss other approaches besides p=0.05--and demonstrate *why* with examples and grounded in experience--this is your time to shine.

Perhaps a little cliche, but I’ve found this holds well: Nail the foundations, then show off your fancy stuff.

How do you get an interview in a really tough job market? This is by far the most common question that I get, and I'm going to give you a story that you're either going to hate or hopefully will be inspired by, because I went through one of the toughest job markets imaginable.

When I was coming out of undergrad, I wanted to really get into Wall Street. When I was in high school and through the start of college, the market was just absolutely booming through the '90s. But then take a look at this chart. Look at the worst part on that chart, where the NASDAQ was down by 80% over like a two and a half year period. And right at that time was when I had my Wall Street interviews.

https://preview.redd.it/e1kvdzhcyyqh1.png?width=1920&format=png&auto=webp&s=799233190b69f6f54413769806dd49df2101afaf

So here's what I did. The first thing was I absolutely lived at my career center. I went to a small liberal arts school called Vassar College that during the run-up would get a pretty decent amount of companies recruiting. But recruiting completely dried up at this part. I spent hours doing mock interviews with my career center, filming them, reviewing film, looking up every alumni who was in the industry, calling them. And especially because it was a small, overlooked school, I did get some alumni that helped me so much with getting me in the door. So absolutely, you should be working those networks.

There was no LinkedIn back then. There was no Indeed.com. I actually opened up Chamber of Commerce websites, finding every registered company with an email address. Many of these were just small, one-person shop consultants, and I wrote to everyone, explaining my situation, how difficult it was finding a job, but also talking about my background and my skill set. Many of these people would then refer me to their friends, try and find something. They really wanted to help. During this time, I actually had two opportunities. I could have moved out to Hawaii through my Chamber of Commerce connections.

This won't work exactly today. Now that LinkedIn exists, it's just so cheap and easy to write to these people. What made my case different was nobody was writing to these people. I was the only one doing it. I was creative. And that's the thing that you need to do. It's not going to be the obvious thing. It's not going to be the easiest thing.

It's a tough break. I came out in a terrible job market. Not everyone has access to parents that can get them an internship, went through the private school circuit, and ended up at Harvard or Yale, and they can just sit back and coast with their alumni network. Some of us have to work harder. And you need to think about how you can be creative. How can you get people's attention to give you a chance and to get you in that door? And if your only answer is to do the exact same thing that tens of thousands of other people are doing, which is just spray and pray on LinkedIn, it's just not going to stand out. If there was an obvious answer, everyone would do it. You need a way to be creative, and that's just the reality that we live in. You're going to have to try harder than other generations have had to.

I got five data science offers in less than two weeks time specifically because I take the HR screen so seriously, and these offers were at Google, Uber, Meta, Figma, and a startup that actually was offering more than all of them.

Typically, people apply for jobs exactly at the time that they need them. And you might go through an entire interview process, an entire interview loop with one company before another company even sees your application and gets back to you. But that's not what I do. When I'm employed, I am taking every single recruiter call within reason that gets back to me. And during these calls, I am making such a strong impression. And this way when I'm looking maybe one year, even two years later, I have a direct line to those recruiters. They look in their notes, they see that I was someone who was very strong, and immediately I'm in the loop.

This has so many positive benefits. The first is that many of these companies use very similar questions across their interviews. So as I'm working through one interview loop, I'm hearing questions that are very likely to come up in another interview loop. The second is competition is amazing for the candidate. When I talk about being in interview loops with other companies or that I'm already getting offers, this dramatically speeds things up on the other companies that I'm talking to.

And then also, you just don't really have the time to get nervous when you're going through so many interviews one after another. If I was just working through one company, after a call I'd be sweating bullets thinking about what I might have done wrong. But when I'm doing so many interviews — and I did 80 rounds in six weeks because of these direct lines to recruiters — it's like next interview up. I don't have time to think about what I did wrong in the last round because I've got the next one coming up very quickly.

I got five data science offers in less than two weeks time specifically because I take the HR screen so seriously, and these offers were at Google, Uber, Meta, Figma, and a startup that actually was offering more than all of them. This is a follow-up to a question asked on my social media videos.

Typically, people apply for jobs exactly at the time that they need them. And you might go through an entire interview process, an entire interview loop with one company before another company even sees your application and gets back to you. But that's not what I do. When I'm employed, I am taking every single recruiter call within reason that gets back to me. And during these calls, I am making such a strong impression. And this way when I'm looking maybe one year, even two years later, I have a direct line to those recruiters. They look in their notes, they see that I was someone who was very strong, and immediately I'm in the loop.

This has so many positive benefits. The first is that many of these companies use very similar questions across their interviews. So as I'm working through one interview loop, I'm hearing questions that are very likely to come up in another interview loop. The second is competition is amazing for the candidate. When I talk about being in interview loops with other companies or that I'm already getting offers, this dramatically speeds things up on the other companies that I'm talking to.

And then also, you just don't really have the time to get nervous when you're going through so many interviews one after another. If I was just working through one company, after a call I'd be sweating bullets thinking about what I might have done wrong. But when I'm doing so many interviews--and I did 80 rounds in six weeks because of these direct lines to recruiters--it's like next interview up. I don't have time to think about what I did wrong in the last round because I've got the next one coming up very quickly.

AI isn't killing data science, but it is killing the junior data science job

The data science job market has been great for people at the senior levels and absolutely brutal for people at the junior levels, and I think AI is a huge part of that.

Here's the thing. When you're using AI tools like Claude Code, it really doesn't make a lot of mistakes on syntax or on very clear instructions, like create a dashboard that shows this time series of results and these metrics. That's pretty easy to do, and it's what a lot of junior data scientists were doing, along with simple machine learning models.

The reason the market is actually quite good at the senior level is that AI can be really bad at making the appropriate analytical and business decision. If you ask it a more generic analysis question, it'll go off on tangents where you're sitting there thinking no, Claude, stop doing these things. It'll make an absolute mess of something you eventually need to get in front of a stakeholder.

When you're more senior and more experienced, you have the intuition to know when to rein Claude in and stop it from going overboard, giving 10,000 times too much information, making data transformations you didn't ask for, or making causal claims that aren't true.

I don't know what the equilibrium is on this. Senior people, especially those who have been in tech a long time, have made money, and they're getting older; they're going to start leaving, retiring, or doing startups. There have been very few junior hires who can come in with the domain expertise to take over.

So there's absolutely a need for data science. Since going back and working at Uber, I've never felt stronger about the need for data scientists, and about the fact that someone non-technical just will not be able to rein Claude in appropriately to make the right decisions.

Where I'm less confident is what happens at the junior level, because in many cases people are coming in and they can't do as well as Claude can. But companies need to invest for the future, because the senior people are going to leave and do other things, and you'll need people with domain expertise to take over. I'm not sure when that hiring is going to come back.

Question asked over on TikTok and sharing here:

Some followers asked how I’m getting 15 interviews in a week when many people are getting zero responses right now. My honest answer was one you might not like: my cold online applications have been practically useless. While 4 years ago I got 7 offers mostly through online applications, this time nearly all of my interview loops (approximately 20) have come through direct channels.

What worked for me was playing the long game during feast times. When I have a job and recruiters reach out, I still took the calls. You can see some of my most viral TT videos are describing first round calls back in 2024 and ppl asking why I’m taking them while employed. I’ve been building those relationships for two to three years, and now reaping benefits during famine times.

The other huge piece is content. I’ve been posting on LinkedIn for years, a combination of learning in public and sharing expertise when I have it. That built a network of nearly 15k in tech with hundreds at Amazon alone. So when I casually posted that I was job searching, both hiring managers and other data scientists offered referrals before I even asked.

First, coming out of a master's program, you need to have a killer project that you can discuss in depth during a 30-45 min past project deep dive interview. You'll have a FAR better time with this if you can find a professor in the program doing the type of work you want to do that is open to bringing on RAs.

At Michigan, my RA experience has been paying off for decades: I had closer access to professors than I'd ever have from classes alone, access to data that would have been difficult to obtain, experience working on real-world important projects, and building closer relationships with the other RAs whom we would all learn from and some of whom I still talk to over a decade later.

Second, start writing to \*recent alumni\* who completed the program during a challenging job market like this one. Ask them how the program prepared them to enter the job market, was it worth the time and cost, and do they have any advice on must-take courses or professors to work with? The benefit to this is not only fresh information, but you're starting a long-game networking relationship with someone you'd eventually share a common alumni network, that works in your target field, and without sounding like someone doing a referral 'spray and pray' to everyone with a DS job.

Jonathan at [https://www.whatstheimpact.com/\](https://www.whatstheimpact.com/)

How can you tell if an expensive master's degree program may be worth the cost?

First, coming out of a master's program, you need to have a killer project that you can discuss in depth during a 30-45 min past project deep dive interview. You'll have a FAR better time with this if you can find a professor in the program doing the type of work you want to do that is open to bringing on RAs.

At Michigan, my RA experience has been paying off for decades: I had closer access to professors than I'd ever have from classes alone, access to data that would have been difficult to obtain, experience working on real-world important projects, and building closer relationships with the other RAs whom we would all learn from and some of whom I still talk to over a decade later.

Second, start writing to *recent alumni* who completed the program during a challenging job market like this one. Ask them how the program prepared them to enter the job market, was it worth the time and cost, and do they have any advice on must-take courses or professors to work with? The benefit to this is not only fresh information, but you're starting a long-game networking relationship with someone you'd eventually share a common alumni network, that works in your target field, and without sounding like someone doing a referral 'spray and pray' to everyone with a DS job.

First, coming out of a master's program, you need to have a killer project that you can discuss in depth during a 30-45 min past project deep dive interview. You'll have a FAR better time with this if you can find a professor in the program doing the type of work you want to do that is open to bringing on RAs.

At Michigan, my RA experience has been paying off for decades: I had closer access to professors than I'd ever have from classes alone, access to data that would have been difficult to obtain, experience working on real-world important projects, and building closer relationships with the other RAs whom we would all learn from and some of whom I still talk to over a decade later.

Second, start writing to *recent alumni* who completed the program during a challenging job market like this one. Ask them how the program prepared them to enter the job market, was it worth the time and cost, and do they have any advice on must-take courses or professors to work with? The benefit to this is not only fresh information, but you're starting a long-game networking relationship with someone you'd eventually share a common alumni network, that works in your target field, and without sounding like someone doing a referral 'spray and pray' to everyone with a DS job.

Jonathan at https://www.whatstheimpact.com/

First, coming out of a master's program, you need to have a killer project that you can discuss in depth during a 30-45 min past project deep dive interview. You'll have a FAR better time with this if you can find a professor in the program doing the type of work you want to do that is open to bringing on RAs.

At Michigan, my RA experience has been paying off for decades: I had closer access to professors than I'd ever have from classes alone, access to data that would have been difficult to obtain, experience working on real-world important projects, and building closer relationships with the other RAs whom we would all learn from and some of whom I still talk to over a decade later.

Second, start writing to *recent alumni* who completed the program during a challenging job market like this one. Ask them how the program prepared them to enter the job market, was it worth the time and cost, and do they have any advice on must-take courses or professors to work with? The benefit to this is not only fresh information, but you're starting a long-game networking relationship with someone you'd eventually share a common alumni network, that works in your target field, and without sounding like someone doing a referral 'spray and pray' to everyone with a DS job.

I've been thinking a lot about how broken the job application process is these days. Four years ago, I received 7 data science offers; a majority of those came through direct cold applications. This past Spring, I started about 15 interview loops (receiving 5 offers including 2 FAANG), but just 2 interview loops initiated from online applications. Nearly all were initiated by recruiter outreach over the past year or two.

**It’s the disconnect between interview success through backdoor channels and online application callback rates that suggest the latter is practically meaningless.**

Some examples that stand out:

I was auto-rejected for a DS role at Google; weeks later, a recruiter miraculously overturned ATS and I ultimately received an offer.

I was auto-rejected at Airbnb; a company where I was offered FTE roles multiple times as a contractor, and had another hiring manager ask me if I wanted to switch teams in case they had an FTE role open up.

I was auto-rejected for a marketing DS role at Apple; afterward a separate recruiter reached out insisting that they and a hiring manager thought I was qualified for a Staff MLE (I'm not an MLE) and requesting an interview 🤦‍♂️ .

There are two stages to getting jobs: (i) getting interviews and (ii) passing interviews. I have a LOT of answers for (ii), but when struggling candidates ask me how to break into interviews, I'm often left saddened, leaving them with long-term advice like brand-building and networking.

I'd love to see some research on this: At our biggest employers in tech, what share of jobs are you filling from online applications vs referrals or recruiter outreach? How are you tracking gatekeeping and the incredible risk of personal bias?

Ironically, the oft-mocked 'Boomer adage' about knocking on doors, resume in hand, may soon be back in fashion.

Jonathan @ https://www.whatstheimpact.com/

I was asked over on Tiktok how I prep for coding interviews using Claude instead of grinding LeetCode.

There are two things to prepare: SQL and Python. For SQL, I already know the syntax and the topics that come up. CTEs, aggregate functions, window functions, date manipulation. So I’ll ask Claude to give me rapid fire syntax problems on those, and then separately ask for business-framed problems based on the company I’m interviewing with.

Python works the same way. Rapid fire syntax on dictionaries and list comprehensions, then pandas fundamentals like groupbys, aggregations, joins and merges. I’ll layer some simple business questions on top of that.

The other Python format is “define a function,” and those are trickier because they can be anything. But in practice a lot of them turn out to be statistical. Bootstrapping, building confidence intervals, writing loops for a Monte Carlo simulation. If you’re preparing for a data science interview, that’s the bucket most people underprepare for.

You can check out all I know about DS interviews over on https://www.whatstheimpact.com

I've spent the past two years giving mock interviews as a DS coach before jumping back into the job market. While concurrently working for Airbnb, I received quite a bit of recruiter outreach, which I took as networking opportunities. I also built up my LinkedIn network to over 10k connections by sharing regularly.

Once I was ready to intentionally job hunt, I was able to rapidly ramp up interviews, reaching out to recruiters and folks in my network when I didn't hear back from online applications or where recruiters had moved on since our previous connections.

This allowed me to play a massive game of calendar tetris: navigating 6-7 weeks of HR intro calls, technical rounds, and final panel rounds so that I could obtain multiple competing offers. The competition also pushed companies to quickly move forward with technical and final round decisions and scheduling.

Ultimately, i received offers at Google, Uber, Meta, Attentive, and Figma before withdrawing from four additional panel rounds for a mix of product and marketing DS roles, and a mix of senior and staff levels.

More importantly, I took very detailed notes of questions received, how I answered, follow-up probes and whether or not I passed the rounds. Working closely with AI, I was then able to dramatically improve my responses, noting that patterns of questions kept appearing over and over again.

For example, I was asked to deep dive into a single past project across nearly every single company...but from different angles. Sometimes it was a technical deep dive, but other times it was more behavioral driven. I set up my story so that it left appropriate threads no matter who was the interviewer or their goals.

There were also foundational stats and experimentation questions, while case study formats expanded beyond what used to be so common in the past: Evaluate a feature launch, consider product sense, develop metrics, suggest experiments, identify a problem with the experiment (e.g. spillovers), and then discuss how to handle those obstacles to measurement. While those problems still exist, there are a number of different ways that various case study archetypes could highlight different components for in-depth probing.

I put everything I learned from these 6-7 weeks of interviewing, patterns of questions, how I prepared including methods for training with AI into a digital product, which I share on my website (via my username). I recognize that folks are coming from different circumstances: different countries, or just out of undergrad and struggling financially. If that's the case and you want a discount code, just message me. For folks going for multiple-six-figure roles, I think this is a fair price.

Happy to answer questions here between my last flood of coaching call requests scheduled today before my start my new role next week.

I've spent the past two years giving mock interviews as a DS coach before jumping back into the job market. While concurrently working for Airbnb, I received quite a bit of recruiter outreach, which I took as networking opportunities. I also built up my LinkedIn network to over 10k connections by sharing regularly.

Once I was ready to intentionally job hunt, I was able to rapidly ramp up interviews, reaching out to recruiters and folks in my network when I didn't hear back from online applications or where recruiters had moved on since our previous connections.

This allowed me to play a massive game of calendar tetris: navigating 6-7 weeks of HR intro calls, technical rounds, and final panel rounds so that I could obtain multiple competing offers. The competition also pushed companies to quickly move forward with technical and final round decisions and scheduling.

Ultimately, i received offers at Google, Uber, Meta, Attentive, and Figma before withdrawing from four additional panel rounds for a mix of product and marketing DS roles, and a mix of senior and staff levels.

More importantly, I took very detailed notes of questions received, how I answered, follow-up probes and whether or not I passed the rounds. Working closely with AI, I was then able to dramatically improve my responses, noting that patterns of questions kept appearing over and over again.

For example, I was asked to deep dive into a single past project across nearly every single company...but from different angles. Sometimes it was a technical deep dive, but other times it was more behavioral driven. I set up my story so that it left appropriate threads no matter who was the interviewer or their goals.

There were also foundational stats and experimentation questions, while case study formats expanded beyond what used to be so common in the past: Evaluate a feature launch, consider product sense, develop metrics, suggest experiments, identify a problem with the experiment (e.g. spillovers), and then discuss how to handle those obstacles to measurement. While those problems still exist, there are a number of different ways that various case study archetypes could highlight different components for in-depth probing.

I put everything I learned from these 6-7 weeks of interviewing, patterns of questions, how I prepared including methods for training with AI into a digital product, which I share on my website (via my username). I recognize that folks are coming from different circumstances: different countries, or just out of undergrad and struggling financially. If that's the case and you want a discount code, just message me. For folks going for multiple-six-figure roles, I think this is a fair price.

Happy to answer questions here between my last flood of coaching call requests scheduled today before my start my new role next week.

I've spent the past two years giving mock interviews as a DS coach before jumping back into the job market. While concurrently working for Airbnb, I received quite a bit of recruiter outreach, which I took as networking opportunities. I also built up my LinkedIn network to over 10k connections by sharing regularly.

Once I was ready to intentionally job hunt, I was able to rapidly ramp up interviews, reaching out to recruiters and folks in my network when I didn't hear back from online applications or where recruiters had moved on since our previous connections.

This allowed me to play a massive game of calendar tetris: navigating 6-7 weeks of HR intro calls, technical rounds, and final panel rounds so that I could obtain multiple competing offers. The competition also pushed companies to quickly move forward with technical and final round decisions and scheduling.

Ultimately, i received offers at Google, Uber, Meta, Attentive, and Figma before withdrawing from four additional panel rounds for a mix of product and marketing DS roles, and a mix of senior and staff levels.

More importantly, I took very detailed notes of questions received, how I answered, follow-up probes and whether or not I passed the rounds. Working closely with AI, I was then able to dramatically improve my responses, noting that patterns of questions kept appearing over and over again.

For example, I was asked to deep dive into a single past project across nearly every single company...but from different angles. Sometimes it was a technical deep dive, but other times it was more behavioral driven. I set up my story so that it left appropriate threads no matter who was the interviewer or their goals.

There were also foundational stats and experimentation questions, while case study formats expanded beyond what used to be so common in the past: Evaluate a feature launch, consider product sense, develop metrics, suggest experiments, identify a problem with the experiment (e.g. spillovers), and then discuss how to handle those obstacles to measurement. While those problems still exist, there are a number of different ways that various case study archetypes could highlight different components for in-depth probing.

I put everything I learned from these 6-7 weeks of interviewing, patterns of questions, how I prepared including methods for training with AI into a digital product, which I share on my website (via my username). I recognize that folks are coming from different circumstances: different countries, or just out of undergrad and struggling financially. If that's the case and you want a discount code, just message me. For folks going for multiple-six-figure roles, I think this is a fair price.

Happy to answer questions here between my last flood of coaching call requests scheduled today before my start my new role next week.

I've spent the past two years giving mock interviews as a DS coach before jumping back into the job market. While concurrently working for Airbnb, I received quite a bit of recruiter outreach, which I took as networking opportunities. I also built up my LinkedIn network to over 10k connections by sharing regularly.

Once I was ready to intentionally job hunt, I was able to rapidly ramp up interviews, reaching out to recruiters and folks in my network when I didn't hear back from online applications or where recruiters had moved on since our previous connections.

This allowed me to play a massive game of calendar tetris: navigating 6-7 weeks of HR intro calls, technical rounds, and final panel rounds so that I could obtain multiple competing offers. The competition also pushed companies to quickly move forward with technical and final round decisions and scheduling.

Ultimately, i received offers at Google, Uber, Meta, Attentive, and Figma before withdrawing from four additional panel rounds for a mix of product and marketing DS roles, and a mix of senior and staff levels.

More importantly, I took very detailed notes of questions received, how I answered, follow-up probes and whether or not I passed the rounds. Working closely with AI, I was then able to dramatically improve my responses, noting that patterns of questions kept appearing over and over again.

For example, I was asked to deep dive into a single past project across nearly every single company...but from different angles. Sometimes it was a technical deep dive, but other times it was more behavioral driven. I set up my story so that it left appropriate threads no matter who was the interviewer or their goals.

There were also foundational stats and experimentation questions, while case study formats expanded beyond what used to be so common in the past: Evaluate a feature launch, consider product sense, develop metrics, suggest experiments, identify a problem with the experiment (e.g. spillovers), and then discuss how to handle those obstacles to measurement. While those problems still exist, there are a number of different ways that various case study archetypes could highlight different components for in-depth probing.

I put everything I learned from these 6-7 weeks of interviewing, patterns of questions, how I prepared including methods for training with AI into a digital product, which I share on my website (via my username). I recognize that folks are coming from different circumstances: different countries, or just out of undergrad and struggling financially. If that's the case and you want a discount code, just message me. For folks going for multiple-six-figure roles, I think this is a fair price.

Happy to answer questions here between my last flood of coaching call requests scheduled today before my start my new role next week.

I coach a lot of data scientists on interviews, have recently completed 80 interview rounds with multiple offers, and there's a behavioral question that comes up constantly: "Tell me about a time you pushed back on a stakeholder." Pretty much every company asks some version of it, and most candidates think they're answering it well. The difference is in the leveling.

What a good answer looks like is completely different depending on what level you're interviewing for. And if you're going for a staff role but giving a senior-level answer, you're leaving a ton of money on the table. We're talking the difference between $300-400K and $500-700K+ total comp at top tech companies.

At the mid level, pushing back basically just means you had too much work and had to say no to something.

At the senior level, you should have an actual prioritization framework. Something like: keeping the product working and helping users comes first, then projects that move revenue, then your own team's work before you start helping other teams. If you can articulate that clearly, that's a solid senior answer.

Staff is where it gets hard. I had an interviewer at a top tech company tell me directly after a staff DS loop: "there needs to be pain." What they actually want to hear is that you've been in a situation where multiple stakeholders wanted your help, they disagreed on which project mattered more, and you had to make that call yourself — without looping in your manager. That's the part people miss. It's not just about saying no, it's about owning a genuinely uncomfortable decision and living with the outcome.

Not everyone will have staff-level experiences, and that's totally fine. Senior-level IC is a fine terminal role at many companies where you can stay without being pushed out.