Two candidates describe Barclays Data Scientist interviews in three steps: a recruiter call or online assessment, a technical round on code, ML and statistics, and a closing behavioral or senior conversation.
$130K
Avg. Base Comp
$150K
Avg. Total Comp
3 rounds
Typical Rounds
Not reported
Process Length
Two candidates describe Barclays Data Scientist interviews that moved from an opening screen to a technical round and then a conversation about fit. The details differ, so it helps to read them as two separate paths rather than one fixed script.
One candidate, who received an offer, started with a recruiter call and then did a two-hour screen-shared technical session. It included exploratory analysis in Jupyter, a review of code the interviewers had written to spot errors and suggest improvements, and designing a machine learning workflow from scratch. Fundamentals came up as well: OLS assumptions, Lasso and Ridge, the Python GIL and multithreading, plus CNNs and transformers. The last step was a half-hour with a senior director, built around STAR-style stories and a discussion of AI in banking.
The other candidate, who did not receive an offer, described three stages: an online assessment covering aptitude, logical reasoning and quantitative ability, with some finance-related and data interpretation items; a technical interview built around their resume; and an HR and behavioral round. The technical interviewer pushed on why particular models and methods were chosen and touched on Python, SQL, machine learning, statistics and basic finance and economics. One question asked which metrics you would use to analyse a company.
Across both accounts, be ready to explain your own work and reasoning in detail, whether that means defending a resume project or critiquing someone else's code live.
Synthesized from 3 candidate reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Barclays process.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Barclays
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Attribution Rules | |
| Find the Missing Number | |
| Bagging vs Boosting | |
| Compute Variance | |
| Sort Strings | |
| CNNs vs Intensity-Based Features | |
| Priority Queue Using Linked List | |
| Prime Numbers Identification | |
| Digital Marketing Metrics | |
| Bias vs. Variance Tradeoff | |
| Data Preparation for Imbalanced Data | |
| Overfit Avoidance | |
| String Palindromes | |
| Impossibly Iterative Fibonacci | |
| Safe Deployments | |
| Why Do You Want to Work With Us | |
| Justify a Neural Network | |
| Your Strengths and Weaknesses | |
| A/B Test Power Size | |
| Dropbox Database | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Employee Salaries | |
| Merge Sorted Lists | |
| Subscription Overlap | |
| Comments Histogram | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Monthly Customer Report |
Synthesized from candidate reports. Individual experiences may vary.
One candidate began with a straightforward recruiter call to scope the role and their background. The other began with an online assessment covering aptitude, logical reasoning and quantitative ability, along with some technical and finance-related questions and data interpretation and problem-solving items.
One candidate had a two-hour screen-shared session: exploratory analysis in Jupyter, a review of code to find errors, and an end-to-end ML workflow design, plus questions on OLS assumptions, Lasso and Ridge, the Python GIL and deep learning. The other had a resume-based interview covering Python, SQL, ML, statistics and basic finance, with follow-ups on model choices.
One candidate had a half-hour session with a senior director focused on values, STAR examples and AI in banking. The other had an HR and behavioral round covering why Barclays and this role, strengths and weaknesses, teamwork, project challenges and career goals, with several follow-up questions.
