Instacart data scientist candidates report experimentation and causal-inference cases, SQL and statistics screens, take-home analyses and panel reviews. One reported process ran a little over two months.
$195K
Avg. Base Comp
$300K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Instacart data scientist interviews, as candidates describe them, lean toward experimentation and causal inference in a marketplace setting. One candidate was asked how to measure an ultra-fast express delivery rollout in a single city. The discussion narrowed quickly into which formula to use and which variables to focus on. The same candidate reported questions on diff-in-diff, synthetic control and establishing causality from observational data. A senior candidate was asked how to assess a reduced delivery fee in one market. That candidate said they leaned too heavily on a randomized trial when observational methods were the better fit, and they were rejected after that first round.
Several candidates describe a technical screen on SQL and statistics with an A/B testing emphasis, followed by a take-home or case study and a panel or virtual onsite. Reported onsite pieces include:
Reports differ by candidate and level, so the exact loop may vary.
SQL can take an unusual form. One candidate was shown a ChatGPT-generated query and asked to find what was wrong with it, so practice reading and critiquing queries as well as writing them. Another candidate described a design discussion on a personalization algorithm for product recommendations. It covered feature engineering and model choices and was followed by a behavioral round.
One candidate described a process of a little over two months that ended in a hiring pause. Practice A/B testing, causal methods and clear presentation of recommendations, and keep other options active while you wait.
Synthesized from 9 candidate reports by our editorial team.
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Real interview reports from people who went through the Instacart process.
I interviewed at Instacart for a Data Scientist role, and the process felt comprehensive but intense. It started with a 1-hour live coding round on CodeSignal. The problem was medium LeetCode difficulty—nothing exotic, but it required clean, readable code and clear communication of my approach. I managed to solve it, which felt like a real accomplishment, but I knew that was just the beginning.
The ML round came next, clocking in at 45 minutes. This is where things got more interesting and less scripted. We dove into core machine learning concepts—I remember being asked to name data imputation techniques, and the conversation evolved from there into discussions about probability, linear algebra, regression, and how to handle overfitting. The interviewers were professional and seemed genuinely interested in my reasoning, not just my answers. They also walked me through building a small pipeline from scratch: loading a dataset, cleaning it, handling missing values, selecting features, fitting a model, and evaluating performance. It was practical and realistic, which I appreciated.
What surprised me most was the depth of the follow-up questions. They didn't just want to know if I could name concepts—they wanted me to explain the assumptions behind my choices, walk through how I'd debug something if it went wrong, and discuss alternative approaches. We also spent time reviewing a previous project of mine, and they asked sharp questions about what I'd do differently and why.
Ultimately, I didn't get an offer. The rejection came after the ML round, and unfortunately Instacart doesn't provide feedback, which was frustrating after putting in the effort across multiple technical rounds. I wish I'd known going in that the pipeline-building exercise would be so hands-on—I could have prepped my approach to missing values and feature selection more deliberately.
Prep tip from this candidate
Be ready to build and explain a complete ML pipeline from data cleaning through model evaluation—not just theory. Also prepare concrete examples of data imputation techniques and be ready to discuss trade-offs between them, plus deep dives into regularization, overfitting, and linear algebra concepts as they apply to real problems.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
| Question | |
|---|---|
| Experiment Validity | |
| 500 Cards | |
| Button AB Test | |
| Decreasing Comments | |
| Raining in Seattle | |
| Impression Reach | |
| Lazy Raters | |
| Netflix Retention | |
| WAU vs Open Rates | |
| Network Experiment Design | |
| Instagram TV Success | |
| Delivery Estimate Model | |
| Random Bucketing | |
| Group Success | |
| Normalize Grades | |
| Fair Coin | |
| Generate Shopping List from Recipes | |
| Found Item | |
| Ride Coupon | |
| Marketing Channel Metrics | |
| Precision and Recall | |
| Expected Tests | |
| Three Zebras | |
| Estimated Rounds | |
| Comparing Search Engines | |
| Stop Words Filter | |
| Median Probability | |
| Biased five out of six | |
| Secret Wins |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report a first technical screen covering SQL and statistics, with A/B testing featuring prominently in some accounts. One senior candidate instead had a first round with the hiring manager that included experimentation questions and a case study. Another candidate was asked to find errors in a ChatGPT-generated SQL query.
Two candidates report a take-home stage. One analyzed a dataset; the other worked through a feature rollout with two test groups and a control. The expectation appears to be a clear recommendation tied to business impact, which candidates then defended in a later discussion.
Candidates describe presenting take-home work to a panel or in a virtual onsite review. Reported discussion focused on how they approached the problem, explained their reasoning and connected the results to business decisions, rather than on a single right answer.
Reported later rounds include: - rapid-fire statistics and hypothesis testing; - product questions about KPIs moving in different directions; - a personalization design discussion covering feature engineering and model choices; - behavioral conversations about past experience. Not every candidate reports every round, so prepare across the range.
Candidates report mixed outcomes, including an offer, a declined offer, rejections and one hiring pause. One candidate described a process of a little over two months, followed by a wait of more than a week for an update. Plan for possible delays and keep other applications moving.
