Data Scientist
Practice data science: statistics, SQL, pandas, A/B testing, causal thinking, machine learning models and explaining insights to business teams.
- Questions
- 100
- Interview Qs
- 30
- Judged problems
- 76
- Skills
- 9
About the role
A Data Scientist uses data to help a business make better decisions. Day to day you ask the right questions, pull data with SQL, clean and explore it with Python, and find patterns that explain what is happening and why. You turn messy questions like "why did sales drop last month?" into clear analyses with numbers people can trust.
Much of the work is statistics and experimentation: designing A/B tests, choosing metrics, checking whether a result is real or just noise, and estimating the effect of a change when you cannot run an experiment. You also build predictive models, such as churn, demand or risk models, and work with engineers to put useful ones into production.
Communication is as important as technical skill. A good data scientist explains findings simply, shows uncertainty honestly, and recommends actions. In 2026 many data scientists also use LLMs to speed up analysis and to work with text data, while still checking every result carefully.
Practise by level
Open the full question bank (100 questions) →Mock interview
Screening round
4 questions · 4 min each · pass 50%
mediumTechnical round
5 questions · 4 min each · pass 60%
hardFinal round
6 questions · 3 min each · pass 70%
Coding problems for data scientists
- Best K-Day Sales Windoweasy
- Best Single Trade Profiteasy
- Binary Classifier Metricseasy
- Coastline Length of the Landeasy
- Cumulative Totalseasy
- Fixed Deposit Maturity Amounteasy
- Min-Max Feature Scalingeasy
- Most Common Allowed Wordeasy
- Rotate a Matrix Clockwiseeasy
- Sensor Moving Averageeasy
- Staircase With Allowed Strideseasy
- Best Match by Cosine Similaritymedium
SQL problems
- Average Score per Subjecteasy
- Campaign Click-to-Conversionmedium
- Class Rank by Total Scoremedium
- Cumulative Hires by Yearmedium
- Daily Active Users with a 3-Day Averagemedium
- Loans by Missed Installmentsmedium
- Month-over-Month Revenuemedium
- Repeat Buyers Within 30 Daysmedium
- Salary Share of Departmentmedium
- Students Above Their Class Averagemedium
Skills in this track
Learning roadmap
- 1
SQL and Python foundations
Write SQL with joins, GROUP BY and window functions. Use pandas and NumPy to clean, reshape and summarise data.
- 2
Statistics and probability
Learn distributions, sampling, confidence intervals, hypothesis tests, p-values and common traps like multiple testing and selection bias.
- 3
Exploratory analysis and visualisation
Explore data with plots, find outliers and missing values, and build clear charts and dashboards that answer one question each.
- 4
Experimentation and causal inference
Design A/B tests with power analysis and guardrail metrics. Learn difference-in-differences, matching and regression adjustment for observational data.
- 5
Machine learning and communication
Build and evaluate models with scikit-learn, explain them with tools like SHAP, and present findings with clear recommendations.
Top Data Scientist interview questions
- Explain what a p-value is to a product manager, and one common way it is misunderstood.
- When would you report the median instead of the mean? Give a business example.
- What is the difference between correlation and causation? How would you test if a change causes an outcome?
- How do you handle missing values in a dataset?
- How do you present a data analysis to non-technical stakeholders?
- Explain the trade-off between precision and recall, and how you would pick a threshold for a real model.