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.
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Real interview reports from people who went through the Barclays process.
I was asked what metrics you would use to analyse a company, a question that sits between finance and data science and is a good sign of how Barclays approaches this role. The process had three stages. The first was 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.
The technical interview was built around my resume and projects. The interviewer asked me to explain what I had built and why I chose particular models and methodologies, then pushed on my reasoning. Python, SQL, machine learning concepts, and statistics came up, as did basic finance and economics. Most of my answers led to follow-up questions, so anything I listed on my resume had to be something I could defend in detail rather than just describe.
The final round was HR and behavioural. It opened with "tell me about yourself" and moved through strengths and weaknesses, why Barclays, why this role, teamwork, challenges during projects, and career goals. Several of these answers were also followed up.
I did not receive an offer. The interviews felt structured and conversational, and the interviewers seemed more interested in whether I understood the concepts than whether I had memorised answers. If I were preparing again, I would spend less time polishing generic talking points and more time being able to walk through every project decision from start to finish.
Prep tip from this candidate
Be ready to walk through every project on your resume end to end, including why you chose each model and methodology, since most answers were followed by detailed cross-questioning. Also brush up on basic finance and economics concepts and a framework for analysing a company's metrics, as these came up alongside the technical and statistics questions.
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Topics based on recent interview experiences.
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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.
