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

Ola Cabs, one of India’s leading ride-hailing platforms, operates in multiple countries, including Australia and New Zealand. Known for its data-driven approach to optimizing ride experiences, Ola relies on skilled Data Analysts to manage and analyze vast datasets. If you’re preparing for a Data Analyst role at Ola, here’s a detailed breakdown of their interview process and the types of questions you can expect.

OLA Data Analyst Interview

These questions will enhance your knowledge and help you to discover your weaknesses in various topics.

Interview Process

The Ola Data Analyst 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 Analysis 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 find customers who haven’t placed any orders in the last six months?

SELECT c.customer_id, c.name
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id AND o.order_date >= DATE_SUB(CURDATE(), INTERVAL 6 MONTH)
WHERE o.customer_id IS NULL;

2) How can you count the total number of products sold for each category?

SELECT p.category_id, SUM(o.quantity) AS total_sold
FROM products p
JOIN order_details o ON p.product_id = o.product_id
GROUP BY p.category_id;

3) How can you find employees who report to the same manager?

SELECT manager_id, GROUP_CONCAT(name) AS employees
FROM employees
GROUP BY manager_id
HAVING COUNT(*) > 1;

4) How do you retrieve the order that has the highest total value?

SELECT order_id, SUM(price * quantity) AS total_value
FROM order_details
GROUP BY order_id
ORDER BY total_value DESC
LIMIT 1;

5) How can you find customers who have ordered the same product more than once?

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

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R/Python – 7/10

1) Write a Python function to remove outliers from a dataset using the Interquartile Range (IQR) method.

2) Write a Python function to count missing (NaN) values in each column of a Pandas DataFrame.

3) Write a Python function to normalize a Pandas DataFrame column using Min-Max scaling between 0 and 1.

4) Write a Python function to find the correlation matrix of a Pandas DataFrame.

5) Write a Python function to train a Linear Regression model using scikit-learn and predict values.

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Statistics/ML

1) What Cross-Validation Technique is Best for Time Series Data?

Unlike regular k-fold cross-validation, time series data has a chronological order that must be maintained. The best techniques are:

1. Time Series Split (Expanding Window) – The training set grows progressively, and validation sets remain ahead in time.
2. Rolling Window Validation – Uses a fixed-size training window that moves forward over time.

👉 Best Practice: Avoid standard k-fold cross-validation because it shuffles data, which isn’t suitable for time-dependent datasets.

2) What is Regularization & Why is it Useful?

Regularization is a technique to prevent overfitting by adding a penalty to large model coefficients.

🔹 Types:

• L1 (Lasso): Shrinks some weights to zero, making the model sparse.
• L2 (Ridge): Shrinks all weights but keeps them non-zero, reducing complexity.

👉 Why is it useful? It improves generalization, ensures better predictions on unseen data, and helps avoid overfitting.

3) Why is Data Cleaning Important? Can Data be Processed Without It?

Data cleaning ensures high-quality, reliable data for analysis.

🔹 Importance:

• Removes duplicates, missing values, and outliers.
• Standardizes inconsistent formats.
• Improves model accuracy and reliability.

Can we process data without cleaning?
Yes, but the results will be inaccurate, biased, or misleading. Garbage in, garbage out!

4) How to Assess a Good Logistic Regression Model?

Key Metrics for Evaluation:

1. Accuracy – Overall correctness of predictions.
2. Precision & Recall – Especially important for imbalanced data.
3. AUC-ROC Curve – Measures how well the model distinguishes between classes.
4. Confusion Matrix – Checks false positives/negatives.
5. Multicollinearity Check – Use VIF (Variance Inflation Factor) to detect correlation between predictors.

5)  How to Develop a Plagiarism Detection Model?

 Approach:

1. Preprocess Text Data – Remove punctuation, stopwords, stemming.
2. Convert Text to Numerical Form – Use TF-IDF, Word2Vec, or BERT embeddings.
3. Measure Similarity – Apply Cosine Similarity, Jaccard Similarity, or N-grams.
4. Train a Classification Model – Detect plagiarized vs. non-plagiarized content using Random Forest or Deep Learning (LSTMs, Transformers).
5. Evaluate & Improve – Check precision-recall, accuracy, and optimize for better detection.

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Case Study

Problem Statement:

Ola wants to improve its demand forecasting model to ensure that the right number of drivers are available at the right locations and times. Your task as a Data Analyst is to analyze past ride data, identify demand patterns, and provide insights to optimize driver allocation and reduce customer wait times.

Dataset Overview:

You have access to a dataset containing historical ride requests and driver availability data. The dataset includes:

• Ride_ID – Unique identifier for each ride
• Date_Time – Timestamp of the ride request
• Pickup_Location – Location where the ride was requested
• Drop_Location – Destination of the ride
• Ride_Status – Whether the ride was completed, canceled, or no driver was available
• Surge_Pricing_Applied – Whether surge pricing was applied (Yes/No)
• Number_of_Drivers_Available – Number of active drivers near the pickup location
• Customer_Wait_Time – Time taken for a driver to arrive
• Traffic_Condition – Level of traffic congestion at the time of request (Low/Medium/High)
• Weather_Condition – Weather status (Clear, Rainy, Foggy, etc.)

Key Questions to Answer:

1. What factors impact ride demand and availability?

• Do peak hours (morning and evening) show higher ride demand?
• How does traffic congestion impact ride completion rates?
• Does bad weather (e.g., heavy rain) affect driver availability?

2. How can Ola improve driver allocation to reduce cancellations?

• Can Ola predict high-demand zones and ensure more drivers are available?
• How can Ola reduce driver idle time while improving customer experience?
• Should Ola incentivize drivers to be available in areas with higher ride requests?

3. Can surge pricing be optimized to balance demand and supply?

• Does surge pricing effectively increase driver availability?
• How does surge pricing impact customer cancellations?
• Should Ola adjust surge pricing based on traffic and weather conditions?

Key Insights & Business Recommendations

1. Understanding Ride Demand Trends

• Peak Hours Have the Highest Demand: Demand is highest during morning (7–10 AM) and evening (5–9 PM) commute hours, requiring better driver availability.
• Bad Weather Increases Ride Cancellations: Rainy and foggy conditions lead to fewer available drivers and more cancellations, indicating the need for weather-based driver incentives.
• High Traffic Leads to Longer Wait Times: In cities with high congestion, customer wait times increase significantly, leading to customer dissatisfaction and ride cancellations.

2. Optimizing Driver Allocation

• AI-Powered Demand Prediction: Ola should implement real-time demand prediction models to ensure drivers are proactively sent to high-demand zones.
• Dynamic Driver Incentives: Offering higher earnings per ride during peak hours and bad weather can improve driver availability and reduce cancellations.
• Reducing Idle Time with Smart Allocation: Ola can use historical ride data to recommend optimal waiting locations for drivers, minimizing the time between consecutive trips.

3. Optimizing Surge Pricing Strategies

• Surge Pricing Works Best in High-Traffic Areas: In high-traffic zones, surge pricing increases driver availability but also leads to customer drop-offs if the fare is too high.
• Dynamic Pricing Based on Customer Demand Elasticity: Instead of flat surge pricing, Ola should adjust surge rates based on past customer behavior to find the right balance between affordability and driver earnings.
• Weather-Based Surge Adjustments: Since bad weather reduces driver availability, Ola should offer dynamic surge pricing with driver bonuses rather than increasing fares for customers.

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