Company: SAP
Designation: Data Scientist
Year of Experience Required: 0 to 4 years
Technical Expertise: SQL, Python/R, Statistics, Machine Learning, Case Studies
Salary Range: 10 LPA – 25 LPA

SAP, a German multinational software corporation, is a global leader in enterprise software solutions. Known for its ERP (Enterprise Resource Planning) software, SAP helps businesses manage operations and customer relations efficiently. If you’re preparing for a Data Science role at SAP, here’s a detailed breakdown of their interview process and the types of questions you can expect.

SAP Data Science Interview Questions

The following questions are similar to the ones asked in the actual interview. Make sure to go through them thoroughly.

Interview Process

The SAP 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 find orders where the total amount is exactly double the previous order by the same customer?

SELECT order_id, customer_id, total_amount, prev_total
FROM (
SELECT order_id, customer_id, total_amount,
LAG(total_amount) OVER (PARTITION BY customer_id ORDER BY order_date) AS prev_total
FROM orders
) AS order_comparison
WHERE total_amount = 2 * prev_total;

2) How do you find customers who have ordered every product at least once?

SELECT customer_id
FROM orders
GROUP BY customer_id
HAVING COUNT(DISTINCT product_id) = (SELECT COUNT(DISTINCT product_id) FROM products);

3) How can you find the month where each customer spent the highest amount?

SELECT customer_id, order_month, total_spent
FROM (
SELECT customer_id, MONTH(order_date) AS order_month, SUM(price * quantity) AS total_spent,
RANK() OVER (PARTITION BY customer_id ORDER BY SUM(price * quantity) DESC) AS rnk
FROM orders
GROUP BY customer_id, MONTH(order_date)
) AS ranked_spending
WHERE rnk = 1;

4) How do you find products that were only ordered once in total?

SELECT product_id, name
FROM order_details
GROUP BY product_id
HAVING COUNT(*) = 1;

5) How can you retrieve employees who have the same salary as another employee but belong to a different department?

SELECT e1.employee_id, e1.name, e1.salary, e1.department_id, e2.department_id AS other_department
FROM employees e1
JOIN employees e2
ON e1.salary = e2.salary AND e1.department_id <> e2.department_id;

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R/Python – 7/10

1) Write a Python function to find the most frequently occurring element in a given list.

2) Write a Python function to extract the year from a date column in a Pandas DataFrame.

3) Write a Python function to find the second largest number in a list without using built-in sorting

4) Write a Python function to calculate cosine similarity between two vectors.

5) Calculate the Mean Absolute Error (MAE) Between Two Lists

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Statistics/ML

1) How to find the correlation between a categorical variable and a continuous variable?

Finding the correlation between a categorical variable and a continuous variable depends on the type of categorical data:

• For ordinal categorical variables (ordered categories like “low,” “medium,” “high”), use Spearman’s Rank Correlation because it measures monotonic relationships.
• For nominal categorical variables (unordered categories like “red,” “blue,” “green”), use ANOVA (Analysis of Variance) or Point Biserial Correlation (if the categorical variable has only two categories).
• Another approach is encoding techniques like one-hot encoding or label encoding to convert categorical variables into numerical form and then apply Pearson correlation.

2) If through training all the features in the dataset, an accuracy of 100% is obtained but with the validation set, the accuracy score is 75%. What should be looked out for?

This situation indicates overfitting—the model has learned the training data too well but fails to generalize to unseen data. Steps to mitigate overfitting:

• Regularization: Apply L1 (Lasso) or L2 (Ridge) regularization to prevent excessive fitting.
• Feature Selection: Remove highly correlated or irrelevant features to simplify the model.
• More Training Data: If possible, collect more data to improve generalization.
• Data Augmentation: For image/text data, introduce variations to training data.
• Reduce Model Complexity: Use a simpler model with fewer parameters.
• Cross-validation: Use k-fold cross-validation to ensure robustness.
• Dropout (for deep learning models): Apply dropout layers to reduce reliance on specific neurons.

3) R dialect has a few bundles for tackling a specific issue. How would you settle on a choice on which one is the best to utilize?

When choosing an R package for a specific problem:

1. Check CRAN Popularity: Popular packages like ggplot2, dplyr, and caret have strong community support.
2. Read Documentation: Well-documented packages with clear examples are preferable.
3. Performance Benchmarks: Compare speed and efficiency using built-in functions like microbenchmark.
4. Community Support: Packages with active GitHub repositories and Stack Overflow discussions are more reliable.
5. Compatibility: Ensure it integrates well with other packages you use.
6. Long-term Maintenance: Packages with frequent updates are less likely to be deprecated.

4) While working on a model, what among them is more important—Model Accuracy or Model Performance?

Model performance is more important than accuracy because:

• Accuracy alone is misleading in imbalanced datasets. A 95% accurate model predicting “no fraud” in a fraud detection case is useless.
• Performance considers precision, recall, F1-score, and AUC-ROC, which are better indicators of real-world effectiveness.
• Context matters: In medical diagnosis, recall (sensitivity) is more crucial, whereas in spam detection, precision is important.
• Use the right metric: For regression, RMSE or MAE might be better than R².

5) What are the steps for wrangling and cleaning data before applying machine learning algorithms?

The key steps in data wrangling and cleaning include:

1. Handling Missing Data – Use imputation (mean, median, mode) or delete rows/columns with too many missing values.
2. Handling Outliers – Detect using Z-score or IQR and remove or cap extreme values.
3. Encoding Categorical Variables – Use one-hot encoding or label encoding.
4. Feature Scaling – Apply normalization (Min-Max Scaling) or standardization (Z-score).
5. Feature Engineering – Create new features that improve predictive power.
6. Dealing with Duplicates – Remove duplicate rows to avoid biased training.
7. Text Preprocessing – Remove stopwords, stemming, and lemmatization for NLP tasks.
8. Balancing Data – Use SMOTE for oversampling or undersampling techniques.

Always evaluate models using multiple performance metrics suited to the problem.

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Case Study

Problem Statement:

SAP wants to predict which enterprises are likely to adopt their ERP software based on historical customer data and market trends. Your task as a Data Scientist is to analyze this data, identify key factors influencing software adoption, and provide actionable insights to improve SAP’s sales and marketing strategies.

Dataset Overview:

You have access to a dataset containing historical enterprise software adoption details. The dataset includes:

• Company_ID – Unique identifier for each company
• Industry_Type – Sector (Finance, Manufacturing, Retail, Healthcare, etc.)
• Company_Size – Small, Medium, or Large enterprise
• Annual_Revenue – Company’s total revenue
• IT_Spending (%) – Percentage of revenue spent on IT solutions
• Previous_ERP_Usage – 1 if the company has used an ERP before, 0 if not
• Cloud_Readiness_Score – A score (0-100) indicating how ready the company is for cloud-based solutions
• Decision_Making_Time (days) – Average time taken to finalize a software purchase
• Customer_Support_Importance – Rating of how important customer support is to the company
• Adoption_Status – 1 if the company adopted SAP ERP, 0 if they did not

Key Questions to Answer:

1. What factors influence enterprise software adoption?

• Do large enterprises adopt SAP ERP more frequently than small businesses?
• Is higher IT spending associated with a greater likelihood of adoption?
• Does previous ERP usage increase the chances of adopting SAP solutions?

2. How can SAP improve its software adoption rate?

• Should SAP offer customized ERP solutions for different industries?
• Can SAP use machine learning to identify high-potential leads?
• Should SAP focus more on cloud-readiness as a key sales factor?

3. How can SAP optimize its marketing and sales strategy?

• Should SAP prioritize industries with faster decision-making cycles?
• Can SAP target companies with lower cloud readiness by offering hybrid solutions?
• How can SAP enhance customer support to attract more businesses?

Key Insights & Business Recommendations

1. Identifying Software Adoption Factors

• Large Enterprises Are More Likely to Adopt SAP ERP: Companies with over $500 million in revenue show a higher adoption rate, indicating that SAP should focus its sales efforts on large enterprises.
• Previous ERP Experience Increases Adoption: Companies that have used ERP software before are more likely to switch to SAP. SAP should target businesses looking for an ERP upgrade.
• Cloud Readiness Impacts Decision Making: Companies with a Cloud Readiness Score above 70 are more likely to adopt SAP’s cloud-based ERP solutions, highlighting the need for cloud education initiatives.

2. Improving SAP’s Software Adoption Strategy

• AI-Based Lead Scoring: SAP can develop predictive models to identify high-potential customers based on IT spending, industry type, and decision-making time.
• Industry-Specific ERP Solutions: Customizing ERP features for different industries (e.g., retail, healthcare) can make SAP more appealing to businesses with unique needs.
• Hybrid Cloud ERP Offerings: Many companies with low cloud readiness scores hesitate to adopt fully cloud-based solutions. Offering hybrid ERP models can increase adoption.

3. Optimizing SAP’s Marketing & Sales Strategy

• Target Industries with Shorter Decision Cycles: Industries like retail and manufacturing make faster software adoption decisions, making them ideal targets for SAP sales teams.
• Emphasize Customer Support in Marketing: Companies that rate customer support as a top priority are more likely to choose SAP. SAP should highlight its world-class support services in its marketing campaigns.
• Increase Personalized Outreach: Using data-driven insights, SAP can provide customized demo experiences based on a company’s past software usage and IT spending trends.

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