Company: InMobi
Designation: Data Scientist
Year of Experience Required: 0 to 4 years
Technical Expertise: SQL, Python/R, Statistics, Machine Learning, Case Studies
Salary Range: Competitive, based on experience

InMobi, an Indian multinational mobile advertising technology company, is a global leader in providing platforms for businesses to advertise their products and services. Known for its data-driven approach and innovative solutions, InMobi targets specific customer segments to deliver personalized advertising experiences. If you’re preparing for a Data Science role at InMobi, here’s a detailed breakdown of their interview process and the types of questions you can expect.

InMobi Data Science Interview Question

If you are preparing for a Data Analyst Job for inMobi, make sure to go through the following questions.

Interview Process

The InMobi Data Science interview process typically consists of 5 rounds, each designed to evaluate different aspects of your technical and analytical skills:

Round 1 – Telephonic Screening

Focus: Basic understanding of Data Science concepts, SQL, and Python/R.
Format: You’ll be asked to explain your projects and solve a few coding or SQL problems.

Round 2 – Walk-in/Face-to-Face Technical Round

Focus: Advanced SQL, coding, and problem-solving.
Format: You’ll solve problems on a whiteboard or shared document.

Round 3 – Project Analysis

Focus: Deep dive into your past projects.
Format: You’ll be asked to explain your approach, tools used, and the impact of your work.

Round 4 – Case Studies

Focus: Business problem-solving and data-driven decision-making.
Format: You’ll be given a real-world scenario and asked to propose solutions.

Round 5 – Hiring Manager Round

Focus: Cultural fit, communication skills, and long-term career goals.
Format: Behavioral questions and high-level discussions about your experience.

Difficulty of Questions

SQL – 8/10

1) How can you retrieve customers who have placed at most 3 orders?

SELECT customer_id, COUNT(order_id) AS total_orders
FROM orders
GROUP BY customer_id
HAVING total_orders <= 3;

2) How do you find the top 3 products based on total sales quantity?

SELECT product_id, SUM(quantity) AS total_sold
FROM order_details
GROUP BY product_id
ORDER BY total_sold DESC
LIMIT 3;

3) How can you find the employees who have been working the longest in the company?

SELECT employee_id, name, joining_date
FROM employees
ORDER BY joining_date ASC
LIMIT 5;

4) How do you find orders with total amounts within a specific range?

SELECT order_id, SUM(price * quantity) AS total_amount
FROM order_details
GROUP BY order_id
HAVING total_amount BETWEEN 200 AND 500;

5) How can you find customers who have purchased more than one type of product?

SELECT customer_id, COUNT(DISTINCT product_id) AS unique_products
FROM orders
GROUP BY customer_id
HAVING unique_products > 1;

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R/Python – 7/10

1) Write a Python function to calculate the Pearson correlation coefficient between two lists of numbers.

2) Write a Python function to find and return duplicate rows in a Pandas DataFrame.

3) Write a Python function to convert categorical data in a Pandas DataFrame column into numerical labels using Label Encoding.

4) Write a Python function to compute a confusion matrix for a classification model.

5) Write a Python function to find the most frequent element in a given list.

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Statistics/ML

1) Difference Between Supervised and Unsupervised Learning

To understand how machine learning models learn, consider these key differences:

• Supervised Learning – Uses labeled data, where input-output pairs are known (e.g., classification, regression).
• Unsupervised Learning – Uses unlabeled data to identify patterns and structures (e.g., clustering, dimensionality reduction).
• Examples – Supervised: Spam detection, price prediction. Unsupervised: Customer segmentation, anomaly detection.

2) How to Prevent Overfitting in Machine Learning Models

To ensure a model generalizes well to new data, follow these techniques:

• Cross-Validation – Use k-fold cross-validation to test model performance on different subsets.
• Regularization – Apply L1 (Lasso) or L2 (Ridge) regularization to prevent large coefficients.
• More Data – Increase training data to help the model capture true patterns.
• Dropout (for Neural Networks) – Randomly drop neurons during training to reduce dependency.
• Pruning (for Decision Trees) – Remove less important branches to simplify the model.

3) Bagging vs. Boosting in Ensemble Learning

Both techniques improve model performance but work differently:

• Bagging (Bootstrap Aggregating) – Trains multiple models independently on random subsets and averages results (e.g., Random Forest).
• Boosting – Trains models sequentially, where each new model corrects previous errors (e.g., AdaBoost, XGBoost).
• Key Difference – Bagging reduces variance, while boosting reduces bias.

4) Understanding the Bias-Variance Tradeoff

To build a well-generalized model, consider these factors:

• High Bias (Underfitting) – Model is too simple, leading to poor performance on both training and test data.
• High Variance (Overfitting) – Model is too complex, performing well on training data but poorly on test data.
• Solution – Find a balance by adjusting model complexity, using cross-validation, and applying regularization.

5) Purpose and Benefits of PCA (Principal Component Analysis)

PCA helps in dimensionality reduction while preserving key information:

• Transforms Features – Converts correlated variables into independent principal components.
• Reduces Dimensionality – Helps in handling high-dimensional data for efficiency.
• Improves Performance – Reduces overfitting and speeds up training.
• Use Cases – Image compression, feature selection, and exploratory data analysis.

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Case Study

Problem Statement:

InMobi wants to improve its ad click-through rate (CTR) prediction model to increase ad engagement and revenue. Your task as a Data Scientist is to analyze historical ad impression data, identify key features influencing CTR, and propose an approach to optimize ad targeting.

Dataset Overview:

You have access to a dataset containing historical ad impressions and user interactions. The dataset includes:

• Ad_ID – Unique identifier for each advertisement
• User_ID – Unique identifier for the user shown the ad
• Timestamp – Date and time of the ad impression
• Ad_Category – Category of the ad (E-commerce, Travel, Gaming, etc.)
• Device_Type – Type of device used (Mobile, Tablet, Desktop)
• Operating_System – OS of the device (iOS, Android, Windows, etc.)
• Location – City or region of the user
• Previous_Clicks – Number of times the user has clicked on ads in the past
• Ad_Position – Placement of the ad (Top, Middle, Bottom of the screen)
• CTR_Label – 1 if the user clicked the ad, 0 if not

Key Questions to Answer:

1. What factors influence ad click-through rate (CTR)?

• Does ad category affect CTR?
• Are users on mobile devices more likely to click ads than those on desktops?
• How does ad position on the screen impact CTR?

2. How can InMobi improve ad targeting for higher engagement?

• Can InMobi use user behavior data to serve more relevant ads?
• How does historical click behavior impact future ad interactions?
• Should InMobi adjust ad placement dynamically based on CTR patterns?

3. How can InMobi optimize real-time bidding (RTB) for advertisers?

• Can high-CTR users be prioritized in bidding strategies?
• How does location-based targeting impact ad effectiveness?
• Should ad delivery frequency be adjusted for better engagement?

Key Insights & Business Recommendations

1. Understanding Factors Affecting CTR

• Mobile Users Have Higher CTR: Ads shown on mobile devices receive more clicks than those on desktops, indicating mobile-first ad strategies are beneficial.
• Ad Position Matters: Ads placed at the top of the screen get significantly higher clicks compared to ads in the middle or bottom.
• User Behavior Drives Engagement: Users with higher past click activity tend to engage more with ads, suggesting behavior-based targeting can increase CTR.

2. Improving Ad Targeting Strategies

• AI-Powered Personalized Ad Recommendations: Using machine learning models, InMobi can analyze past user interactions and serve highly relevant ads.
• Dynamic Ad Placement Adjustments: By continuously analyzing CTR patterns, InMobi can optimize ad positions in real-time to increase engagement.
• Location-Based Ad Targeting: Users in urban regions show higher engagement with ads related to e-commerce and travel, while users in suburban areas prefer local services and entertainment.

3. Optimizing Real-Time Bidding (RTB) for Advertisers

• Bidding More for High-CTR Users: Advertisers should prioritize bidding on users with historically high engagement, improving ROI.
• Reducing Ad Fatigue: Showing the same ad repeatedly decreases CTR. InMobi should implement ad frequency capping to avoid user fatigue.
• A/B Testing for Ad Effectiveness: Running A/B tests on different ad creatives and placements can provide insights into optimal ad designs for better CTR.

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