UBS Data Analyst candidates describe online assessments, recorded video questions, a SQL and Python technical round, and manager or HR conversations. One candidate reported about a month from start to offer.
$100K
Avg. Base Comp
$115K
Avg. Total Comp
3-4 rounds
Typical Rounds
1 month
Process Length
Candidates who describe UBS Data Analyst interviews report a staged process in which motivation and communication count alongside SQL and Python. The reports differ in format, so treat this as a picture of what has appeared rather than a fixed loop.
Early screening is often online. One candidate completed numerical and verbal reasoning sections before a video interview with five questions. A separate analyst-titled report with a Python and SQL focus described a behavioral questionnaire and then a pre-recorded interview of seven questions. That candidate had two minutes to think and two to answer each question, with no retakes.
The technical conversation is where reports diverge. One Data Analyst candidate was given two tables and asked about joins, aggregations, filtering and window functions. The same candidate then covered pandas operations and how to build, evaluate and interpret predictive models. Another was questioned by a panel from the chief data division about the difference between an index and a benchmark, the income from holding stocks versus bonds, and SQL experience.
Later rounds lean on fit. Candidates describe conversations with a line manager, HR or a recruiter, and a hiring manager. Topics include stakeholder disagreements, competing priorities, favorite and least favorite projects, and explaining technical ideas to non-technical colleagues. One candidate who reported a timeline said the process took about a month from start to offer. Outcomes in these reports are mixed. Prepare a few concrete project stories and a specific answer for why UBS.
Synthesized from 13 candidate reports 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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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Ubs
Which model would perform better and why?
| Question | |
|---|---|
| Decision Tree Evaluation | |
| Hurdles In Data Projects | |
| Lasso vs Ridge | |
| Data Preparation for Imbalanced Data | |
| Shortest Path Algorithms | |
| Company Acquisition Choice | |
| Stakeholder Communication | |
| Your Strengths and Weaknesses | |
| Martingale Strategy | |
| Student Tests | |
| Statistically Significant Test | |
| 2nd Highest Salary | |
| Employee Salaries | |
| Random SQL Sample | |
| Bagging vs Boosting | |
| Size of Joins | |
| Assumptions of Linear Regression | |
| P-value to a Layman | |
| Duplicate Rows | |
| Type-ahead Search | |
| HHT or HTT | |
| Type I and II Errors | |
| Random Forest Explanation | |
| Justify a Neural Network | |
| Unified Inbox | |
| Scalped Ticket | |
| WAU vs Open Rates | |
| Missing Housing Data | |
| Three Zebras |
Synthesized from candidate reports. Individual experiences may vary.
Some candidates start with online reasoning questions or a questionnaire before a recorded video interview. One reported numerical and verbal reasoning followed by five video questions. Another described a behavioral multiple-choice questionnaire and seven recorded questions, with two minutes to think and two to answer, and no retakes.
One Data Analyst candidate worked through two tables with joins, aggregations, filtering and window functions, then pandas and questions on building and interpreting predictive models. An analyst-titled report described scenario-based Python and SQL questions. Another candidate faced a chief data division panel asking about index versus benchmark, income from stocks versus bonds, and SQL experience.
Candidates describe conversations with a line manager, HR or a recruiter, and a hiring manager. Reported topics include favorite and least favorite projects, working with stakeholders, disagreements, competing priorities, teamwork, leadership, career aspirations, and the team's projects. One candidate described the hiring manager round as a discussion rather than an interrogation.
One candidate who passed the video stage described an assessment centre with a group exercise and individual interviews, and did not receive an offer. Another reported three virtual rounds taking about a month from start to offer, with a verbal answer within a couple of days and the formal offer about two weeks later.
