Landing a product data science role requires demonstrating strong data science and analytics skills, business acumen, and communication abilities. While you may have the hard skills, nailing the interview questions is vital to getting the job. This article compiles a list of common data science questions asked for product data scientist interviews to help you prepare.
Key Technical Questions:
- Metrics and KPIs — Expect questions about deciding the right metrics to track for products. Be ready to suggest metrics to measure engagement, retention, growth etc. and talk through how you would calculate them.
- Statistical testing — Know the specifics of how A/B testing works, how to determine statistical significance, and how to analyze the results. Brush up on the commonly used statistical tests.
- Machine learning — Be prepared to talk through the machine learning models you have worked with. Know how to evaluate model performance, handle imbalanced datasets, and avoid overfitting.
- Data workflows — Questions may test your knowledge of pipelines for collecting, storing, processing and analyzing data at scale. Understand architectures like Hadoop, Spark.
- Data quality — Expect questions about dealing with missing data, anomalies, duplicates etc. Know imputation methods, outlier detection techniques, and data validation methods.
Key Product Sense Questions:
- User research — Be ready to suggest ways to identify user pain points through analytics, surveys, user interviews etc. Know qualitative and quantitative methods.
- Evaluating ideas — Know how you would design experiments and use data to evaluate new product ideas or features. Discuss metrics you would track.
- Optimization — Prepare ideas for optimizing key funnels like signup, onboarding, purchase etc. Suggest testing different flows, UX, copy etc.
- Feature usage — Expect questions on measuring feature adoption, retention and engagement. Know cohort analysis.
- Causal analysis — Know techniques like regression to analyze causal relationships and isolate feature impact.
Key Communication Questions:
- Presenting analysis — Review how to present analyses and results visually. Practice summarizing insights.
- Influencing decisions — Be ready to discuss strategically communicating results to influence product decisions.
- Translating complexity — Prepare stories of simplifying complex data science concepts for non-technical audiences. Emphasize clarity.
- What are some key metrics you would track for an ecommerce website or app? How would you measure success?
- How would you detect anomalies or unexpected changes in time series data?
- Explain how A/B testing works. How do you determine statistical significance?
- What are some advantages of cohort analysis vs aggregate analysis? When would you use each?
- How would you handle missing or corrupted data in a dataset?
- What are some machine learning models you have worked with? How did you evaluate their performance?
- How would you deal with data skews or imbalanced classes in a classification problem?
- How would you identify the top user pain points or friction areas in a product?
- Describe a project where your analysis led to a key product insight or improvement.
- How would you evaluate new feature ideas? What metrics would you look at?
- What KPIs are important for measuring engagement in a social media app? How would you calculate them?
- How would you A/B test changes to an onboarding flow? What would you measure?
- How would you communicate A/B test results to product managers or leadership?
- Tell me about a time you had to simplify a complex analysis for non-technical stakeholders.
- How do you balance rigor with delivering insights fast enough to enable quick product decisions?
Preparing responses and examples demonstrating your proficiency in these key areas will prove your value in product data science roles. Use this list to guide your interview preparation. Show both your statistical chops and business intuition. With the right preparation, you can ace your upcoming product data science interviews!