u/CryoSchema

just wanted to share a recent google interview experience for a product data scientist role, specifically its technical screens.

for this role, it has 2 technical screens, which are the same format as the 2 technical rounds for the on-site. it’s not like other big tech companies where the screen acts as the filter, and the on-site rounds are usually more of a technical deep-dive.

first technical round is sql + a/b testing, second technical round is ML.

1. a/b testing sample question: test metric moved, figure out if it’s a real effect
2. sql: window functions, multi-table joins, aggregations
3. ML : no implementation, about model evaluation, closer to product sense

there was also an HR round, which shouldn’t be underestimated because it has questions like how you’d communicate insights to non-technical stakeholders. so you’d have to answer using plain language, no terms or jargon like p-value or lift.

another tip is to ask your recruiter what the team is working on so you can closely align your stories/experiences with what they’re solving.

more details from the full experience, including sample questions, follow-ups, and prep notes are in the link i shared above.

just wanted to share a recent google interview experience for a product data scientist role, specifically its technical screens.

for this role, it has 2 technical screens, which are the same format as the 2 technical rounds for the on-site. it’s not like other big tech companies where the screen acts as the filter, and the on-site rounds are usually more of a technical deep-dive.

first technical round is sql + a/b testing, second technical round is ML.

1. a/b testing sample question: test metric moved, figure out if it’s a real effect
2. sql: window functions, multi-table joins, aggregations
3. ML: no implementation, about model evaluation, closer to product sense

there was also an HR round, which shouldn’t be underestimated because it has questions like how you’d communicate insights to non-technical stakeholders. so you’d have to answer using plain language, no terms or jargon like p-value or lift.

another tip is to ask your recruiter what the team is working on so you can closely align your stories/experiences with what they’re solving.

more details from the full experience, including sample questions, follow-ups, and prep notes are in the link i shared above.

hi, everyone. there’s been some recent changes with stripe’s data scientist interview process. so i'm sharing the experience with how different it is now, especially around team matching and how the rounds are structured.

key changes:

• team matching now happens before the onsite
• if you don’t pass the onsite, no second chances with a different team
• ai assistant integrated throughout the processes

process:

1. screening with hiring manager
2. technical screen
3. resume gets matched against teams
4. case study
5. individual interviews: product sense, sql + product metrics, collaborative, behavioral

*there was no recruiter call since it was through a referral

the case study round focused on stripe’s products and merchant segments. you’re essentially asked to diagnose failures + identify growth areas + propose improvements. since this happens after team matching, it will be tied to that specific team’s work/product area.

also, it’s not clear yet why the ai assistant sits through the rounds & what it does. you just need to be clear & concise since redundancy/repetitions in the transcript may be interpreted negatively.

this full resource for the stripe ds interview has a more detailed breakdown of the experience, including what the other rounds covered, how the team matching played out, and the feedback received.