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What's the Impact?
Causal Research for Marketing, Product, and Data Science Partners
Data Science Interview Courses
In April-May 2026, I completed 80+ interview rounds and received 5 offers (Google, Uber, Meta, Figma, Attentive) and withdrew from 4 additional panel rounds. I have so much new information about the entire end-to-end data science interviewing process, with insights on what works and what doesn't.
Importantly, these lessons *can be learned.* I know this because I went 1 for 3 in my first three final rounds and then went 4 for 5 after deep introspection about where I was coming up short.
Back in 2021, I did incredibly well on the job market -- 7 for 7 in final rounds. But it was a much different environment back then. I've again proven that I have a deep, deep understanding of how to get multiple competive offers at some of the most competitive tech companies for roles that specialize in experimentation and causal inference.
I've also had deep success coaching data scientists to offers at Google, Meta, DoorDash, Uber, Airbnb, Stripe, TikTok, Discord, Etsy, and more — junior through staff levels. With my own availability for 1:1 coaching less reliable, I've packaged my knowledge into these interview prep courses.
End-to-End Data Science Interview Prep
This course covers highly detailed and specific patterns I saw repeated during my intense interviews in Spring 2026. Modules cover multiple archetypes for the Case Study, ML modeling rounds, past experience and behavioral rounds, foundational statistics and experimentation, coding challenges (including new AI-assisted rounds), and even the HR screen.
The Data Science Case Study Framework covers exactly what I walk clients through before mock interviews: how to systematically approach any experimentation or causal inference case study, regardless of industry or team. 85 minutes, 6 videos, applicable from junior through staff levels.
Interview and Career Coaching
Jonathan is a senior data scientist. In Spring 2026, he completed over 80 interview rounds in five weeks, securing 5 offers (a mix of staff and senior offers at Uber, Google, Meta, Figma, and Attentive) before withdrawing from 4 additional final rounds. He will be starting at Uber late June 2026.
In 2023, he applied to one company--Airbnb--and received an offer within a week.
In 2021, he went 9 of 10 for technical round case-study and take-home challenge interviews with invitations to the final round. He then went 7 for 7 within a 2-week period before withdrawing for the last two. These companies included Stripe, Meta, Amazon, Uber, fka Twitter, Robinhood, and more. Most recently, Jonathan has passed Staff DS technical rounds at Netflix and Meta before pausing his candidacy to travel the world.
While at Airbnb, Jonathan has expanded his career coaching business with the most popular request being for case-study mock interview support, with clients receiving offers from Junior to Senior to Staff at companies including Meta, AirBnb (2x), Uber (3x), Google (2x), TikTok, Snapchat, Discord, Etsy, BCG, and more.
Jonathan has also done coaching outside of interview prep including resume reviews, advice transitioning into data science, networking, and tutorials related to experimentation, statistics, and observational causal inference. See more: COACHING SERVICES
Jonathan Hershaff, PhD
Jonathan Hershaff is a Data Scientist and Economist with a PhD from the University of Michigan. He has more than a decade of experience in causal modeling, having worked at Airbnb, Stripe, Collage.com (sold e-commerce startup), the Securities and Exchange Commission (SEC), the Federal Reserve (DC), and Wells Fargo Bank.
Dr. Hershaff had research cited in a winning US Supreme Court Case, has testified as an expert economic witness teaching a Grand Jury about investor advisor fraud, published in peer-reviewed journals, and presented at multiple economics and public policy conferences.
While in the tech sector, Dr. Hershaff worked closely with both C-suite executives and senior leadership across marketing, sales, product, and engineering. He also has extensive experience partnering with SEC and DOJ attorneys.
Why Causal Research?
Sales have increased following a new marketing campaign. Conversion rates have fallen after a feature launch or website change. Were these due to the team's changes or external factors out of our control?
Example: Return on ad spend (ROAS) for branded search looks great at a glance, but how many of those conversions would have gone through organic SEO links if paid ads weren't there?
Example: We're considering investing in insurance against a bad outcome for our users, but not sure how much our customers would penalize us if that bad outcome occurs. We certainly don't want to experiment with negative experiences; how do we find the cost of such events?
Causal research can help to answer these difficult questions, even when experimentation is not a viable option and we are left with only observational data. Results can inform marketing budgets, product decisions, headcount, and more.
Request a consultation
Schedule a risk-free, no-obligation, no-sales-pitch call today to discuss your research needs, and whether this partnership is a strong fit.