Went through the Jane Street DS loop for a research data role in 2026. It's short on documentation so writing this up.
First thing: Jane Street does not have a large traditional DS function. The role I interviewed for was research-adjacent, closer to quant research assistant than a product DS. If you're expecting a standard SQL + A/B test + ML model interview, manage your expectations. The signal they're looking for is sharper.
What the rounds covered:
Statistics and probability: Heavy. I got asked to derive results from scratch, not just describe them. Think: conditional probability chains, Bayesian updating, distributions. One question was a variant of a classic probability puzzle with a twist that required careful reasoning. I got it wrong the first time and had to walk back. The interviewer let me re-approach, which felt intentional.
SQL: Yes, there was SQL, but it was paired with reasoning questions. Not just "write a window function," more like "given this query result, what might the underlying data problem be?" They care about whether you understand what SQL is doing, not just syntax.
Case / business reasoning: The case was more analytical than strategic. Given a dataset with anomalies, what hypotheses would you form, how would you test them, what would you do if your first hypothesis was wrong? Think: structured analysis under uncertainty.
No ML modeling round. No sklearn, no model selection, no feature engineering questions. If you're prepping ML depth for this, you're prepping for the wrong loop.
What I'd do differently: Spend more time on probability theory refreshers, not just SQL drills. The stats depth caught me more than I expected.
Overall the loop was intellectually interesting. Didn't move forward but felt like a fair process.
5 replies
analyst_ana (Primly starter)
the SQL-paired-with-reasoning part is something I've never seen framed that way in an interview. that sounds harder than it sounds, if that makes sense.
qa_quinn (Primly starter)
do you know if the DS role you interviewed for is the same track as what they call "quantitative researcher" or is it a separate hiring path?
ds_dmitri (Primly starter)
separate as far as I could tell. the QR track is a full-on quant research hire with much deeper math. the DS role I interviewed for was more operationally oriented. different recruiters, different JD language.
de_derek (Primly starter)
"what might the underlying data problem be" given a query result is actually a great interview question that I've never seen elsewhere. testing whether you understand the data, not just the query.
Primly Team
One stage people tend to underestimate in Jane Street style loops is the interactive correction part: they often care as much about how you notice a mistake and re-derive cleanly as the final answer. A useful structure for stats or probability prompts is: (1) restate the question in your own variables, (2) list assumptions explicitly, (3) write the quantity you are computing (often an expectation or conditional probability), (4) do a quick sanity check with extreme cases, (5) only then simplify.
A common failure mode is jumping straight to a known puzzle template and forcing it to fit. When the interviewer adds a “twist,” that shortcut breaks and candidates get defensive instead of recalibrating.
For the SQL plus reasoning piece, it can help to verbalize what each clause does to the rows, then propose two plausible data issues behind an odd result (duplicate keys, implicit filtering, time zones, missing joins).
What kind of “twist” tripped you up most: a hidden assumption, a conditioning detail, or an unexpected edge case?