MPL Data Analyst Interview: Rounds, Questions and Preparation

What the job involves

As a Data Analyst at an Indian unicorn or high-growth startup like MPL, you will work with large datasets to uncover insights that drive product decisions, user growth, and monetization. Day to day, you might write SQL queries, build dashboards, run A/B tests, and present findings to product managers and business teams. The role demands a mix of technical skills, business acumen, and the ability to communicate data stories clearly.

Role and Technical Knowledge

1. 1

   How would you write a SQL query to find the second highest daily active users (DAU) for a gaming app like MPL?

   Use a subquery with LIMIT and OFFSET, or a window function like DENSE_RANK. Explain the logic and mention handling ties.

2. 2

   What is the difference between a LEFT JOIN and an INNER JOIN? Give an example of when you would use each in a user analytics context.

   LEFT JOIN returns all rows from the left table, INNER JOIN only matching rows. For user analytics, use LEFT JOIN to include users with no transactions, INNER JOIN to analyze only active users.

3. 3

   Explain what A/B testing is and how you would design one to test a new feature in a gaming app.

   Cover hypothesis, control and treatment groups, randomization, sample size, success metrics (e.g., retention, session time), and statistical significance.

4. 4

   What are some key product metrics you would track for a real-money gaming platform? How would you define them?

   Mention DAU, MAU, stickiness (DAU/MAU), ARPU, retention rate, churn, and conversion rate. Define each clearly and relate to business goals.

Practical Scenarios and Problem Solving

1. 5

   You notice a sudden drop in daily active users for a gaming app. How would you investigate the cause?

   Break down by dimensions (platform, geography, user segment), check for technical issues, external events, or changes in user behavior. Suggest data-driven steps.

2. 6

   How would you measure the success of a new referral program launched by a startup?

   Define metrics like number of referrals, conversion rate, cost per acquisition, and incremental lift. Compare with a control group if possible.

3. 7

   Given a dataset of user transactions, how would you identify the most valuable customer segments?

   Use RFM analysis (Recency, Frequency, Monetary value) or clustering. Explain how you would segment and what actions you would recommend.

4. 8

   You have to present a dashboard to non-technical stakeholders. How would you ensure it is effective?

   Focus on clarity, key metrics, intuitive visualizations, and actionable insights. Avoid clutter and use storytelling to highlight trends.

Behavioural and HR Questions

1. 9

   Why do you want to work as a Data Analyst at a high-growth startup like MPL?

   Show passion for data-driven decision-making, interest in gaming or consumer tech, and eagerness to learn in a fast-paced environment.

2. 10

   Describe a time when you used data to solve a problem or make a decision.

   Use the STAR method. Highlight the data you used, the analysis, and the impact. Even a college project or internship example works.

3. 11

   How do you handle feedback or criticism on your analysis?

   Show openness to learning, willingness to iterate, and ability to defend your work with data while being receptive to improvements.

4. 12

   Where do you see yourself in 3-5 years in the analytics field?

   Align your growth with the company's needs, e.g., specializing in product analytics, data science, or leading a team. Show ambition and commitment to learning.

SQL and Python Coding Challenges

1. 13

   Write a SQL query to calculate the 7-day retention rate for users who signed up last month.

   Use a self-join or window functions to identify users active on day 0 and day 7. Define retention clearly and handle date logic.

2. 14

   How would you use Python (Pandas) to clean a dataset with missing values and outliers?

   Mention techniques like imputation, dropping rows, capping outliers, and using describe() to understand data. Explain trade-offs.

3. 15

   Given a table of user sessions, write a SQL query to find users who have logged in on consecutive days.

   Use window functions like LAG to compare dates, or self-join. Filter for consecutive dates and distinct users.

4. 16

   How would you merge two datasets in Pandas and handle duplicate keys?

   Use pd.merge with appropriate how parameter. Discuss checking for duplicates with duplicated(), and strategies like drop_duplicates or aggregation.