Deloitte Data Scientist interviews typically combine a recruiter call, a project-focused technical round, statistics and ML fundamentals, and behavioral or case discussion. Some candidates also report take-homes and long waits between rounds.
$123K
Avg. Base Comp
$141K
Avg. Total Comp
2-4 rounds
Typical Rounds
2-8 weeks
Process Length
Candidates describe the Deloitte Data Scientist interview as centered on explaining your own project work and the reasoning behind your modeling choices. Several reports start with a recruiter or HR conversation, then move to a technical round where you walk through a resume project and defend choices such as model selection, data cleaning, and methodology. Interviewers are described as friendly and more interested in your thinking than in obscure trivia.
The technical content varies by report. Some candidates describe a fairly light conversation: ML basics, the difference between AI, ML and data science, and a short, simple coding exercise. Others report a broader mix, including ridge versus lasso regularization, population versus sample, Python, SQL, AWS, deep learning topics such as CNNs and transformers, RAG and generative AI, and a medium LeetCode-style algorithm problem. One candidate was asked how vector databases differ from relational databases. Another was asked to justify KNN versus decision trees to a non-technical stakeholder, so practice plain-language explanations.
Formats also differ. Some candidates report a churn-prediction take-home discussed with the team afterward, a HackerRank assessment with ML questions, or a dashboard-interpretation exercise aimed at non-technical stakeholders. Behavioral rounds are described as STAR-style questions about teamwork, challenges, motivation, and career goals.
Timing is uneven. Some candidates report about two to three weeks end to end, while another reports roughly two months, and others mention long waits between rounds. Prepare broadly across statistics, ML, coding, and communication, and keep other options open while you wait.
Synthesized from 13 candidate reports by our editorial team.
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Real interview reports from people who went through the Deloitte process.
My interview process at Deloitte for a Data Scientist role spanned about two to three weeks. The first round was an HR call where they asked standard questions about my background, current interests, and where I saw myself going — nothing too intense, just getting to know me. After that came the technical round, which was split into two parts. First, I had to present a data science project I'd worked on. They let me choose any topic as long as it involved data and modeling, so I talked through one of my recent projects and explained my approach. The interviewer asked some follow-up questions about my process, particularly around data cleaning and the methods I'd used, but nothing that felt overly difficult or designed to trip me up. Then came a shorter coding segment, maybe around ten minutes, where I tackled a simple coding question — nothing like a full LeetCode grind, more just demonstrating I could think through a problem. They also asked me about machine learning basics to get a sense of my foundational knowledge. The whole thing felt pretty conversational and collaborative rather than adversarial. The interviewers seemed genuinely interested in understanding my work and how I approach problems, which made it less stressful than I'd expected going in.
Prep tip from this candidate
Focus on being able to clearly articulate a real data science project you've built, including your data cleaning approach and the models you used — that's the backbone of their technical assessment. Have a simple coding problem or two under your belt, but don't over-prepare for complex algorithms; they're more interested in your end-to-end project thinking.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Deloitte
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Merge Sorted Lists | |
| Raining in Seattle | |
| Maximum Profit | |
| Bagging vs Boosting | |
| Size of Joins | |
| Normalize Grades | |
| Using R Squared | |
| Cyclic Detection | |
| Precision and Recall | |
| Missing Housing Data | |
| Find Duplicate Numbers in a List | |
| Hurdles In Data Projects | |
| Assumptions of Linear Regression | |
| Portfolio Platform Architecture | |
| Duplicate Rows | |
| FAQ Matching | |
| Classification and Regression | |
| Slow SQL Query | |
| Data Pipelines and Aggregation | |
| Swap Variables | |
| Yelp-like System | |
| User Event Data Pipeline | |
| Bias vs. Variance Tradeoff | |
| Data Preparation for Imbalanced Data | |
| Subway Machine Learning Model | |
| Multicollinearity in Regression | |
| String Palindromes | |
| Impossibly Iterative Fibonacci | |
| Text Editor With OOP |
Synthesized from candidate reports. Individual experiences may vary.
Candidates typically report an opening call with a recruiter or HR contact covering background, motivation, and career goals. One describes a CV review first. The tone is usually described as standard and conversational rather than technical.
Some candidates report an ML-oriented HackerRank assessment or a churn-prediction classification take-home that is later discussed with the team. Not everyone mentions an assessment, so it may vary by team, level, or timing.
Candidates report project walkthroughs where interviewers probe model choice, data cleaning, and the math behind methods. Reported topics include ML fundamentals, ridge versus lasso, population versus sample, and in some cases deep learning and generative AI. Coding is often described as light, though some candidates reported data structures problems, a medium algorithm question, or Python and SQL.
One candidate's first round paired a background discussion with a case segment built around their own resume projects. Another was asked to interpret business dashboards, spot anomalies, and explain insights for non-technical stakeholders. These may not appear in every process.
Candidates report STAR-style behavioral questions on teamwork, challenges, motivation, and five-year goals, with some processes adding a team or cultural fit round. One candidate reports an HR call to handle offer negotiation, while another reports weeks of slow follow-up.
