The reported UBS Data Analyst process has four conversations: a background screen, a demanding SQL and Python technical interview, an HR discussion, and a hiring-manager conversation focused on projects and communication.
$102K
Avg. Base Comp
$127K
Avg. Total Comp
4 rounds
Typical Rounds
2-4 weeks
Process Length
For a UBS Data Analyst interview, prepare for a process that moves from your experience into applied data work and then back to how you collaborate. One candidate reported four distinct conversations: an initial background screen, a technical round, HR, and a hiring-manager discussion. The screening focused on prior projects, reasons for moving, and fit with the role, so be ready to explain decisions and outcomes from work you know closely.
The technical interview was described as the hardest stage. SQL began with joins, aggregations, and filtering, then shifted toward analytical business problems; the candidate was given two tables and asked to work through SQL questions, including window functions. Python coverage centered on pandas and data manipulation, with attention to the reasoning behind an approach rather than lengthy code. The same interview also included questions about building, evaluating, and interpreting predictive models.
HR covered motivation, communication, stakeholder disagreements, and competing priorities. The hiring-manager conversation explored team projects, problem-solving style, and communicating technical ideas to non-technical people. UBS presents its work as increasingly data-driven, which makes a clear connection between analysis and business context worth practicing. This guide reflects one reported process, so team-level variation is possible.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Ubs process.
The first round was a screening interview. It was mostly about my background: what I had worked on, why I wanted to move, and whether my experience matched the role. It was quite conversational, and I left feeling confident because it was mostly about projects I already knew well.
The second round was the technical interview, and that was definitely the hardest part. They focused mainly on SQL and Python. The SQL questions started with joins, aggregations, and filtering data, but then became more analytical. They wanted to see how I approached business problems, not just whether I knew the syntax. In Python, the questions were centered around data manipulation with pandas, handling datasets, and explaining my thought process rather than writing hundreds of lines of code.
The third interview was with HR. That one was more about motivation, communication, and cultural fit. They asked about situations where I had worked with stakeholders, dealt with disagreements, and managed competing priorities. It wasn't technically difficult, but it required clear examples from my previous experience.
The final round was with the hiring manager. This felt more like a discussion than an interrogation. We talked about the team, the types of projects they were working on, how I like to solve problems, and what I wanted from my next role. They were also trying to understand whether I'd fit into the team and whether I could communicate technical ideas to non-technical people.
Questions asked: I was given two tables and asked to solve a series of typical SQL questions. The interview covered topics such as joins, aggregations, filtering, and window functions, as well as some basic pandas operations. Beyond the coding exercises, they also asked questions about data science concepts and model development, focusing on how I would approach building, evaluating, and interpreting predictive models. Overall, the interview was a mix of SQL, pandas, and general data science knowledge rather than just coding.
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Topics based on recent interview experiences.
Featured question at Ubs
Which model would perform better and why?
| Question | |
|---|---|
| Hurdles In Data Projects | |
| Lasso vs Ridge | |
| Data Preparation for Imbalanced Data | |
| Shortest Path Algorithms | |
| Company Acquisition Choice | |
| Decision Tree Evaluation | |
| Stakeholder Communication | |
| Your Strengths and Weaknesses | |
| Statistically Significant Test | |
| Student Tests | |
| Martingale Strategy | |
| 2nd Highest Salary | |
| Employee Salaries | |
| Bagging vs Boosting | |
| Random SQL Sample | |
| P-value to a Layman | |
| Size of Joins | |
| Random Forest Explanation | |
| Type-ahead Search | |
| Scalped Ticket | |
| WAU vs Open Rates | |
| Three Zebras | |
| Missing Housing Data | |
| Assumptions of Linear Regression | |
| Classification and Regression | |
| Target Indices | |
| Success Measurement | |
| RAG Strict Source Control | |
| HHT or HTT |
Synthesized from candidate reports. Individual experiences may vary.
The candidate reports that the first conversation was a conversational screen about prior work, reasons for changing roles, and whether their experience matched the position. Prepare concise examples from projects you can explain confidently, including your own contribution and the outcome.
The reported technical round covered SQL joins, aggregations, filters, window functions, and analytical business problems using two tables. It also included basic pandas data manipulation and discussion of how to build, evaluate, and interpret predictive models; candidates should explain their reasoning as they work.
The candidate reports questions on motivation, communication, stakeholder disagreements, and competing priorities. Use specific examples that show how you worked with others and made tradeoffs, rather than relying on general statements about teamwork.
The final conversation was described as a discussion of the team, its projects, problem-solving preferences, career goals, and the ability to explain technical ideas to non-technical people. Expect a two-way conversation and be ready to connect your working style to those themes.
