As data science and analytics continue to shape the financial landscape, securing a role at innovative institutions like ING demands a blend of technical prowess, domain knowledge, and problem-solving acumen. In this blog, we’ll delve into common interview questions and provide insightful answers tailored for aspiring candidates aiming to join ING’s data science and analytics team.
Table of Contents
Technical Interview Questions
Question: What is model overfitting?
Answer: Model overfitting occurs when a machine learning model learns to capture noise and random fluctuations in the training data rather than the underlying patterns. This results in a model that performs well on the training data but fails to generalize to unseen data, leading to poor performance on new observations. Overfitting can be mitigated by techniques such as regularization, cross-validation, and using simpler models with fewer parameters.
Question: How can you prevent a Random Forest from overfitting?
Answer: To prevent a Random Forest from overfitting, you can:
- Limit the depth of individual trees or restrict the number of leaf nodes.
- Increase the number of trees in the forest to improve generalization.
- Implement feature selection techniques like limiting the maximum number of features considered for splitting nodes or using feature importance measures to prioritize informative features.
Question: What are the underlying assumptions of a logistic regressor?
Answer: The underlying assumptions of logistic regression include:
- Linearity: The relationship between the independent variables and the log odds of the dependent variable is linear.
- Independence: Observations are independent of each other.
- No multicollinearity: The independent variables are not highly correlated with each other.
- Large sample size: Logistic regression performs better with a large sample size to ensure stable parameter estimates and accurate inference.
Question: Why are correlations between independent variables bad?
Answer: High correlations between independent variables can lead to multicollinearity, which inflates standard errors and reduces the reliability of coefficient estimates. This can obscure the true relationships between predictors and the target variable, making the model less interpretable and potentially less effective in making accurate predictions. Additionally, multicollinearity can make it difficult to identify the most important variables in the model, leading to suboptimal feature selection and model performance.
SQL Interview Question
Question: What is SQL, and why is it important at ING?
Answer: SQL (Structured Query Language) is a standard language for managing and manipulating relational databases. At ING, SQL is crucial for querying customer transaction data, generating reports for regulatory compliance, and analyzing market trends for informed decision-making.
Question: How would you optimize SQL queries for performance?
Answer: SQL query optimization at ING involves techniques like creating indexes on frequently accessed columns, optimizing joins, and using appropriate WHERE clauses to filter data efficiently. Additionally, analyzing query execution plans and avoiding unnecessary sorting or aggregation operations enhances query performance.
Question: Explain the difference between INNER JOIN and LEFT JOIN.
Answer: INNER JOIN returns rows with matching values in both tables based on the join condition, excluding non-matching rows. LEFT JOIN returns all rows from the left table and matched rows from the right table, with null values for non-matching rows from the right table. At ING, both are used for data analysis and reporting purposes.
Question: How do you handle database transactions and ensure data integrity?
Answer: At ING, database transactions are managed using SQL’s ACID properties (Atomicity, Consistency, Isolation, Durability). Transactions are wrapped in BEGIN TRANSACTION and COMMIT/ROLLBACK statements to ensure all or none of the operations are performed, maintaining data integrity and consistency across multiple operations.
Question: Describe your experience with SQL functions and stored procedures.
Answer: I have extensive experience at ING writing SQL functions to perform calculations, manipulate strings, and extract date-related information. Additionally, I’ve developed stored procedures to encapsulate complex SQL logic, improve code modularity, and enhance database security and performance.
Question: How do you handle NULL values in SQL queries and expressions?
Answer: At ING, NULL values in SQL queries are handled using functions like COALESCE and ISNULL to replace NULLs with default values or handle them appropriately in calculations. Additionally, CASE statements are used to conditionally handle NULL values based on specific criteria in SELECT, WHERE, and ORDER BY clauses.
Python and ML Interview Questions
Question: What is Python, and why is it important at ING?
Answer: Python is a high-level programming language used for various tasks including data analysis, automation, and web development. At ING, Python is important for its simplicity, versatility, and extensive libraries like Pandas and NumPy, used for financial data analysis, risk modeling, and automation of banking processes.
Question: How would you handle missing data in a Python Pandas DataFrame?
Answer: In Pandas, missing data can be handled using methods like dropna() to remove rows or columns with missing values, fillna() to fill missing values with specified values, or interpolate() to interpolate missing values based on existing data.
Question: Explain the difference between supervised and unsupervised learning.
Answer: Supervised learning involves training a model on labeled data, where the model learns from input-output pairs. In contrast, unsupervised learning involves training on unlabeled data, where the model tries to find patterns or structures within the data without explicit guidance.
Question: How do you evaluate the performance of a machine-learning model?
Answer: The performance of a machine learning model at ING is evaluated using metrics like accuracy, precision, recall, F1-score, and area under the ROC curve (AUC-ROC) for classification tasks. For regression tasks, metrics like mean squared error (MSE), root mean squared error (RMSE), and R-squared are commonly used.
Question: Describe your experience with feature engineering in machine learning projects.
Answer: In machine learning projects at ING, feature engineering involves transforming raw data into meaningful features that improve model performance. Techniques include one-hot encoding, scaling, binning, and creating new features from existing ones through domain knowledge or automated methods like polynomial features and feature selection algorithms.
Question: How would you handle a class imbalance in a classification problem?
Answer: Class imbalance in classification problems at ING is addressed using techniques such as resampling (over-sampling minority class or under-sampling majority class), using different evaluation metrics (precision-recall curve, F1-score) that are less sensitive to class imbalance, and employing advanced algorithms like ensemble methods or cost-sensitive learning.
Question: Explain the concept of cross-validation in machine learning and its importance.
Answer: Cross-validation is a technique used to assess the performance and generalization ability of a machine learning model. It involves splitting the dataset into multiple subsets, training the model on a portion of the data, and evaluating it on the remaining portion. This helps in estimating the model’s performance on unseen data and identifying potential issues like overfitting.
Behavioral Interview Questions
Que: Why do you want to work at ING
Que: Have you previous experience in Finance
Que: Why did you move to this country?
Que: What gives you energy?
Que: Are you familiar with GitHub?
Que: Do you have a GitHub account?
Que: What are the most recent trends in Machine Learning?
Que: How do you keep up with updates in the world of Machine Learning?
Que: How did you prepare yourself for the transition from Academia to ING (banking)?
Que: Did you do any additional online courses?
Que: Explain the Random Forest algorithm.
Que: At each node of a Random Forest, how is decided which feature is the most discriminative?
Que: How would you identify the most “wealthy” account holders?
Que: What is the most impressive achievement you have realized?
Que: What salary are you looking for?
Que: Which languages do you speak?
Conclusion
Preparing for a data science and analytics interview at ING requires a blend of technical expertise, domain knowledge, and communication skills. By understanding common interview questions and crafting insightful answers like those provided above, aspiring candidates can demonstrate their readiness to contribute to ING’s mission of delivering innovative solutions and driving excellence in the financial industry through data-driven insights and analytics.
