Data Scientist Scenario-Based Questions

1. You are working on a predictive model for customer churn, and your initial results are not as accurate as expected. A key feature is showing little correlation with the target variable. How would you address this issue?

2. A stakeholder asks for a machine learning model that runs in real-time, but you know the data necessary for this model is not available in real-time. How would you handle this request?

3. You identify a significant bias in your training data that skewed your model's predictions. How would you go about correcting this bias?

4. During a presentation of your project findings, a key stakeholder challenges the validity of your results. They claim that the model lacks practical application. How would you respond to this criticism?

5. You receive a large dataset that has numerous missing values. Describe your approach to handling these missing values while ensuring the integrity of your analysis.

6. Your team is divided on the selection of algorithms for a complex problem, with some favoring traditional statistical methods and others advocating for advanced machine learning techniques. How would you approach this situation?

7. While analyzing a dataset, you discover a pattern that suggests potential ethical issues regarding data privacy. How would you proceed with your findings and address these ethical considerations?

8. A client requests insights from their data but has provided vague and unclear objectives. How would you extract more information and clarify their needs for a better analysis?

9. After deploying a model to production, you start receiving feedback that the model's performance is degrading over time. What steps would you take to investigate and remediate the situation?

10. You are tasked with integrating various data sources from different departments, each with its own schema and standards. How would you tackle the challenges of this data integration process?