Instacart Data Scientist Interview Questions

Instacart·Data Scientist·Technical Phone Screen

Jul 2026

Instacart data science interview focused on a classic metric decline investigation scenario. The whole thing was essentially one big open-ended case, and they wanted to see how structured your thinking was across data quality, segmentation, and hypothesis generation.

A key business metric (like orders per day or conversion rate) has dropped noticeably over the past week or two. Walk through how you'd investigate it.

“This is the kind of question where you can go wide fast and lose the thread entirely.”

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Instacart·Data Scientist·Technical Phone Screen

Jul 2026

Instacart data scientist interview with a deep-dive technical question on large-scale data pipeline design. Single question but it covered a lot of ground, felt more like a system design session than a typical DS screen.

Walk through a project where you ingested and processed a dataset of 500 million or more rows (or over 1 TB) end-to-end. Cover storage formats, partitioning, memory and compute constraints, schema evolution, data quality checks, indexing, and your tool choices. Share before/after runtimes and cost, a specific code-level optimization you applied, and how your approach would change if you were limited to a single 32 GB RAM machine.

“This one sprawled in every direction.”

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Instacart·Data Scientist·Take-home Assignment

Jul 2026

Take-home assignment for a DS role at Instacart centered entirely on one big A/B test case about a checkout change for same-day delivery. Dense, multi-part, and the kind of thing that looks manageable until you're three hours in and second-guessing your variance assumptions.

For each metric in the A/B test (conversion rate, AOV, take rate, delivery cost per order), which hypothesis test would you use and why? Walk through the distributional assumptions and how you'd validate them.
Define contribution per order as GMV times take rate minus delivery cost. How would you model delivery cost scaling with order volume and take rate saturation at high discounts, and how do you propagate uncertainty through to a final decision?
Design a segmentation plan to detect heterogeneous treatment effects across dimensions like market, time of day, basket size, and new versus repeat users. How do you control for multiple comparisons while still surfacing segments that are actually useful?
Given the operational risk of manually scaling shopper supply, what optimization objective and constraints would you propose to balance short-term revenue lift against service-level metrics like cancellation rate?

“This is where I spent most of my time and probably overcomplicated it.”

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Instacart·Data Scientist·Technical Phone Screen

Jul 2026

Instacart data science interview with a heavy stats/modeling focus. The core question was about a regression model with a terrible R² and what to do about it, which sounds simple but has a lot of moving parts once you get into inference vs prediction tradeoffs.

You have a linear regression predicting contribution per order with an R² of 0.07. What concrete steps would you take to improve predictive performance without breaking the validity of your inference?
Does adding more covariates reliably increase out-of-sample R²? Walk through how you'd demonstrate this with cross-validation, and what modeling alternatives you'd consider if the goal is still effect estimation.
How would you use nested cross-validation and target-leakage tests to prevent p-hacking when iterating on model specifications?
When is a low R² acceptable for estimating an average treatment effect, and when does it become a real problem for making individual-level predictions?

“This is where I probably spent too long listing things without prioritizing.”

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Instacart·Data Scientist·Technical Phone Screen

Jul 2026

SQL-heavy technical screen for a data scientist role at Instacart. One question, focused on time-series aggregation over order data. Nothing too wild but you need to be comfortable with date functions or you'll fumble it.

Write a SQL query that returns total revenue and order count for the last 8 full calendar weeks, filtering to standard delivery orders only.

“The filter part is easy, the tricky bit is getting 'last 8 full calendar weeks' right.”

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Instacart·Data Scientist·Hiring Manager Screen

Jul 2026

Interviewed for a Data Scientist role at Instacart and got hit with some pretty introspective questions about what I actually want from a job, not just what I can do. Felt more like a values alignment conversation than a technical screen.

What are the top five things you need from your next role, how do you rank them, and what are the minimum thresholds you'd accept for each?
Walk me through how you'd evaluate in the first 90 days whether this role is actually a good fit for you, including what signals you'd look for and what would make you decide it isn't working.
What is one absolute dealbreaker for you in a role, and what is one dimension where you're genuinely flexible?

“This tripped me up more than I expected.”

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Instacart·Data Scientist·Technical Phone Screen

Jul 2026

Technical phone screen for a Data Scientist role at Instacart. One meaty coding problem the whole time, no small talk, just straight into the implementation.

Implement a pivot table operation from scratch: given a list of row dicts and a pivot spec (index columns, column columns, values column, and an aggregation function), return the pivoted result as a nested dict or 2D structure.

“This felt more like a data engineering question than a data science one, which threw me a bit.”

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Instacart·Data Scientist·Technical Phone Screen

Jul 2026

Senior DS loop at Instacart focused entirely on marketplace analytics and experimentation. Four meaty questions across metric debugging, experiment design, result interpretation, and retention cohort analysis. No fluff, no behavioral stuff, just back-to-back case questions the whole time.

A key marketplace metric has dropped over the past two weeks. Walk through how you'd figure out if the decline is real and what's causing it, covering data quality, seasonality, supply-demand dynamics, user segments, and external factors.
Design an experiment to test a new shopper pricing model that pays more during peak hours. Cover the randomization unit, how you'd build comparable market pairs, pre-period balance checks, success and guardrail metrics, and how you'd handle spillover and seasonal noise in the analysis.
The experiment is done. Profit per order (the pre-declared north star) went down, but average order volume went up. Do you recommend launching? How do you think through a negative primary metric against positive secondary ones, and when is a no-launch still the right call?
A dashboard shows D14 retention dropped sharply for the most recent cohort, while older cohorts look fine. How do you figure out if this is a real problem versus an artifact of immature cohorts, ingestion lag, or a metric definition issue?

“This one felt open-ended enough that I spent probably too long on the data quality angle before getting to the interesting stuff.”

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Instacart·Data Scientist·Technical Phone Screen

Jul 2026

A product analytics case from Instacart focused on diagnosing a drop in D14 retention. The whole thing was about separating a real signal from a measurement artifact, which is trickier than it sounds when cohort maturity is involved.

A dashboard shows a sharp decline in D14 retention over the past week. Walk through how you'd figure out if this is a real product problem or just a measurement artifact.

“The maturity angle is what trips people up here and I almost glossed over it too.”

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Instacart·Data Scientist·Technical Phone Screen

Jun 2026

Coding round for a Data Scientist role at Instacart. The problem was a set cover variant dressed up as a shopping optimization question, which took me a second to recognize for what it actually was.

Given a list of required products and a set of shoppers each covering some subset of those products, find the minimum number of shoppers needed so their combined purchases cover every required product. Return -1 if it's not possible.

“I recognized this as set cover pretty quickly, which felt good, but then I second-guessed myself mid-explanation and started rambling about greedy being optimal when it's obviously not.”

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