To succeed, you must understand exactly what the interviewers are looking for in each phase of the conversation. The MSD evaluation is distinctly segmented into three core areas.
Situational and Behavioral Scenarios
MSD places a strong emphasis on how you integrate into a team and handle the realities of corporate data work. This area evaluates your professional maturity, communication skills, and ability to navigate ambiguity in everyday situations. Strong performance looks like providing concise, structured answers that highlight empathy, proactive communication, and a focus on business outcomes.
Be ready to go over:
- Stakeholder Management – How you handle requests from non-technical colleagues who may not fully understand data limitations.
- Prioritization – How you decide which data requests to tackle first when facing multiple urgent deadlines.
- Error Handling – How you communicate when you discover a flaw in your own analysis after it has been shared.
- Cross-functional Collaboration – Working alongside engineering, product, or scientific teams to gather requirements.
Example questions or scenarios:
- "Imagine a stakeholder urgently requests a dashboard by tomorrow, but the underlying data pipeline is broken. What would you do in this specific scenario?"
- "Tell me about a time you had to explain a complex analytical finding to a completely non-technical audience."
- "What would you do if you noticed a discrepancy in a key metric report right before a major leadership presentation?"
Analytical and Logical Reasoning
Before diving into code, MSD interviewers want to see how your brain works. This section tests your pure problem-solving skills, logical deduction, and ability to structure a problem from scratch. Strong candidates do not just jump to an answer; they outline their assumptions, ask clarifying questions, and walk the interviewer through their deductive process.
Be ready to go over:
- Process Structuring – Breaking down a large, ambiguous goal into measurable data steps.
- Logic Puzzles – Everyday brainteasers that test your deductive reasoning and mathematical intuition.
- Metric Design – Defining what to measure to answer a specific business question.
- Data Troubleshooting – Hypothesizing why a specific metric might have suddenly dropped or spiked.
Example questions or scenarios:
- "If our primary sales metric dropped by 15% yesterday, walk me through the exact logical steps you would take to find the root cause."
- "You have a dataset with missing values for a critical variable. Logically, how do you decide whether to drop the rows, impute the data, or leave it as is?"
- Logical brainteasers assessing probability or sequence identification.
Technical Data Skills
The final portion of the evaluation grounds your logical thinking in practical tools. This area verifies that you have the hard skills required to execute the job daily. A strong performance involves writing clean, efficient queries, understanding data structures, and knowing how to visualize results effectively.
Be ready to go over:
- SQL Proficiency – Joins, window functions, aggregations, and subqueries.
- Data Visualization – Best practices for building dashboards in tools like PowerBI or Tableau.
- Data Cleaning – Identifying outliers, handling duplicates, and standardizing formats.
- Basic Statistics – Averages, medians, standard deviation, and basic A/B testing concepts.
Example questions or scenarios:
- "Write a SQL query to find the top 3 selling products per region over the last quarter."
- "How would you design a dashboard to track supply chain bottlenecks? Which visualizations would you choose and why?"
- "Explain the difference between a LEFT JOIN and an INNER JOIN, and give a scenario where you would use each."
