Just recently finished Uber Data Scientist interview, the overall feeling is that the pace is very compact, not only examining the technical details, but also very important to the business understanding and stakeholder communication skills. Unlike many traditional tech companies, Uber's data science positions are more business impact driven, and many cases are closely related to product scenarios, requiring you to quickly come up with analytical ideas that can be put into practice in limited time. Here is a summary of my interview process and the questions I encountered, I hope to help you.
Uber Data Scientist Interview Process Overview
VO (several rounds of video interviews before Virtual Onsite):
- Round 1: Hiring Manager
Resume + Project + a product case - Round 2: Stakeholder Management
Let's start with a question and answer BQ and then move on to the product case - Round 3: Analytics & Experimentation
Para-statistics + experimental design, discussion with delivery scenarios
Onsite (formal live session, 5 rounds):
- Coding (Python/SQL)
- Bar Raiser (DS Director)
- Experimentation (Marketplace Case)
- Cross-functional (dialog with PM)
- HM (resume + past experience)
Detailed question review
VO stage
Round 1: Hiring Manager
This round was mainly about resume and past projects. I thought it was just a general introduction, but the interviewer asked detailed questions, such as "How much real business impact did your model end up delivering?". I was a bit stuck at the beginning, I couldn't remember the numbers clearly. Luckily, Programhelp helped me to sort out the common follow-up questions beforehand, and prepared the paragraph linking the impact indicators and my project in advance, so I went straight down on the spot, or else I would have easily lost points in this round.
The product case is not that difficult, it is to see if you can combine the data to put forward the optimization idea. programhelp reminded: Uber especially valued the "rapid formation of the framework", so I according to the structure of Hypothesis → Metric → Experiment to talk about, the idea is very clear. The interviewer even nodded.
Round 2: Stakeholder Management
This round started off a little tricky. the interviewer started off by asking a collaborative BQ, something like "What would you do if a cross-sector colleague disagreed with your analytic solution?"The question is actually about communication skills. This kind of question is actually to see the ability to communicate, I answered more general, fortunately Programhelp voice reminded me to mention "first understand each other's motivation, and then use the data to support", clinical added this, the effect is immediately different.
Then came the Product Case: how to compare and prioritize the effectiveness of Uber Eats' promotions. Here I got bogged down in the details and thought long and hard about whether to compare user conversion rate or GMV first, which dragged on for a bit too long, but Programhelp reminded me not to beat around the bush, and to throw out an overall framework (user growth vs. profitability), and then refine the metrics step by step. Once I did that, my thoughts went smoothly. The interviewer was obviously more concerned about prioritization rather than specific numbers.
Round 3: Analytics + Experimentation
This round was really quite challenging. The background was that "Leave at door" is used by 60% users, but the rate of lost shipment is 5 times higher and refund is very expensive. Interviewer asked me how to analyze the cause and design the experiment.
My first instinct was to "run a regression model", but halfway through I realized I was being too technical and not getting down to the business side of things, and Programhelp reminded me in the background that I needed to start with the user journey Starting from, for example, "Maybe the floor is too high / the address is vague / the driver left without confirming". I immediately adjusted the direction, from the user behavior, the driver operation, the system process to break down the reasons, the logic was established.
As for the experimental design, I originally only thought of doing A/B test, but Programhelp reminded me that I could add in clustering experiments, such as high-risk area vs low-risk area, to directly make my answers more relevant to the Uber scenario.
Onsite phase
1️⃣ Coding
Coding is actually my biggest worry, the interviewer just said: no Pandas, write two dataframes merge by hand. The interviewer directly said: don't use Pandas, write two dataframes merge by hand, honestly, I don't practice this kind of low-level coding, I almost got stuck at one time, Programhelp remotely reminded me to use dict/hashmap to store the index, and then traverse the merge, the efficiency came up, and the final writing was quite complete. They reminded me to write the most basic join first, and then add aggregation step by step, so as to avoid writing complex query and making mistakes.
2️⃣ Bar Raiser
Director came up and threw a big case: how to launch Uber One project, how to analyze the success, the difficulty is to stand on a very high strategic level, not to dive into the data, I almost fell into the details of "calculating GMV, retention rate". I almost fell into the details of "calculating GMV and retention rate", Programhelp had rehearsed this kind of high-level case for me before, and reminded me that the answer should be divided into three levels: user value, merchant value, and Uber platform value. Once I spread out the three points on the spot, I instantly had an "executive presence" and was able to stabilize this round.
3️⃣ Experimentation (Marketplace Case)
The topic is:Uber Marketplace How do I choose a merchant onboard? It's about modeling and predicting which merchant will bring the most value. The problem is that I started thinking in e-commerce logic and ended up speaking in a very rigid way.Programhelp woke me up in the background: Uber is two-sided marketplaceThe interviewer said, "We need to consider the supply-demand balance, not just pulling merchants. So I immediately changed my mind, from the supply side, demand side, matching efficiency to explain how to model, the interviewer obviously more buy.
4️⃣ Cross-functional (XFN with PM)
The atmosphere in this round was the most relaxed, just talking about the product with the PM. Programhelp reminded me to switch to layman language and use storytelling expressions, such as "business onboarding is like opening a new restaurant, you have to calculate the surrounding traffic and taste". As soon as I changed the expression, the PM was able to catch up immediately, and the communication went smoothly.
5️⃣ Hiring Manager
HM asked about career motivation and the most impactful project in the past. The answer I had prepared was too academic, but Programhelp reminded me to highlight the impact: "My analysis saved the company millions in rebates". It's a much more impressive way to put it.
Summarize
Overall, the Uber DS interview gave me the feeling:
The VO stage is more like "screening".: See if you have basic analytical skills and a stakeholder management mindset.
Onsite examines more depthThe DS is a full-fledged DS, from coding, to marketplace cases, to communicating with PMs.
If you're preparing for an Uber interview, it's recommended:
Coding: Don't just rely on Pandas, be able to handwrite basic operations.
Experimentation: Understand the marketplace model and not just apply the logic of e-commerce/on page optimization.
Stakeholder BQ: Practice clarity of expression, especially in conflict resolution, priority management scenarios.
The secret to easily getting an offer from a big company
If you are preparing for a Data Scientist interview at Uber or a similar top tech organization, don't be a hard ass. Many students are stuck at the case analysis or coding stage, not because they don't know how to do it, but because they don't think fast enough.Programhelp It's available to you:
OA/VO Traceless Remote Assist
Real-time voice alerts + Debug tutorials
High quality case walkthrough
Helping you to minimize pitfalls and improve your pass rate.
