BNY Mellon

Data Scientist I

Data ScientistEMedium

The Data Scientist I interview at BNY Mellon is designed to assess a candidate's foundational knowledge in data science, statistical modeling, machine learning, and programming skills, along with their ability to apply these concepts to solve business problems. The role requires a blend of technical expertise and practical problem-solving abilities.

Timeline

~14 days

Experience

1 - 3 yrs

Salary Range

US$85000 - US$110000

Overall Evaluation Criteria

Technical Skills and Knowledge

Technical proficiency in Python, SQL, and relevant data science libraries (e.g., Pandas, NumPy, Scikit-learn).
Understanding of statistical concepts and their application.
Knowledge of machine learning algorithms, including their strengths, weaknesses, and use cases.
Problem-solving skills and analytical thinking.
Ability to interpret and communicate data insights effectively.
Experience with data visualization tools.
Behavioral competencies such as teamwork, communication, and adaptability.

Problem Solving and Application

Ability to translate business problems into data science solutions.
Experience in data cleaning, preprocessing, and feature engineering.
Proficiency in building, training, and evaluating machine learning models.
Understanding of model deployment considerations (though less emphasis for entry-level).

Communication and Behavioral

Clear and concise communication of technical concepts.
Ability to explain complex findings to both technical and non-technical stakeholders.
Active listening skills and thoughtful responses.
Demonstrated interest in the company and the role.

Preparation Tips

1Review fundamental statistics, probability, and linear algebra.
2Brush up on core machine learning algorithms (e.g., regression, classification, clustering, tree-based models).
3Practice coding in Python, focusing on data manipulation (Pandas) and scientific computing (NumPy).
4Strengthen your SQL skills for data querying and manipulation.
5Prepare to discuss your past projects in detail, focusing on the problem, your approach, the tools used, and the outcome.
6Understand the bias-variance tradeoff, overfitting, and regularization techniques.
7Familiarize yourself with common data science interview questions and practice answering them.
8Research BNY Mellon's business and how data science is applied within the financial industry.
9Prepare questions to ask the interviewer about the role, team, and company culture.

Study Plan

1

Foundational Statistics

Weeks 1-2: Statistics & Probability (Python libraries)

Weeks 1-2: Focus on foundational statistics and probability. Cover topics like descriptive statistics, inferential statistics, hypothesis testing, probability distributions, and Bayesian concepts. Practice problems using Python libraries like SciPy and Statsmodels.

2

Machine Learning Fundamentals

Weeks 3-4: Machine Learning Algorithms (Scikit-learn)

Weeks 3-4: Dive into core machine learning algorithms. Understand supervised learning (linear regression, logistic regression, SVM, decision trees, random forests, gradient boosting) and unsupervised learning (k-means, PCA). Focus on understanding the underlying principles, assumptions, and use cases. Practice implementing these algorithms using Scikit-learn.

3

Python for Data Science

Weeks 5-6: Python for Data Science (Pandas, NumPy, Visualization)

Weeks 5-6: Enhance your Python programming skills for data science. Master Pandas for data manipulation and cleaning, NumPy for numerical operations, and Matplotlib/Seaborn for data visualization. Work on coding challenges related to data wrangling and analysis.

4

SQL Proficiency

Week 7: SQL for Data Analysis

Week 7: Strengthen your SQL skills. Practice writing queries for data extraction, filtering, joining, aggregation, and window functions. Work through common SQL interview problems.

5

Project and Behavioral Preparation

Weeks 8-9: Behavioral & Project Deep Dive (STAR Method)

Weeks 8-9: Prepare for behavioral and project-based questions. Review your resume and select 2-3 key projects to discuss in detail. Use the STAR method (Situation, Task, Action, Result) to structure your answers. Practice explaining technical concepts clearly and concisely.

6

Final Preparation

Week 10: Mock Interviews & Final Review

Week 10: Mock interviews and final review. Conduct mock interviews focusing on technical questions, coding challenges, and behavioral aspects. Review key concepts and company information.

Commonly Asked Questions

Tell me about a challenging data science project you worked on.
How would you approach building a recommendation system?
Explain the difference between L1 and L2 regularization.
What are precision and recall, and when would you use one over the other?
Write a SQL query to find the top 3 customers by total spending.
Describe a situation where you had to deal with imbalanced data.
How do you handle outliers in a dataset?
What is cross-validation and why is it important?
Explain the concept of gradient descent.
How would you design an A/B test for a new website feature?

Location-Based Differences

New York

Interview Focus

Strong emphasis on practical application of statistical concepts.Assessment of proficiency in Python and SQL for data manipulation and analysis.Understanding of core machine learning algorithms and their use cases.

Common Questions

Explain a project where you used Python for data analysis.

Describe your experience with SQL for data extraction and manipulation.

How would you handle missing data in a dataset?

What are the assumptions of linear regression?

Explain the bias-variance tradeoff.

Tips

Be prepared to discuss specific projects in detail, highlighting your contributions and the impact.
Practice SQL queries for common data retrieval and aggregation tasks.
Review fundamental statistical concepts and machine learning algorithms.

London

Interview Focus

Emphasis on communication skills and ability to translate technical findings into business insights.Evaluation of experience with experimental design and model validation.Understanding of feature engineering techniques to improve model performance.

Common Questions

Walk me through a time you had to explain a complex data finding to a non-technical audience.

How do you validate a machine learning model?

What is regularization and why is it used?

Describe your experience with A/B testing.

How do you approach feature engineering?

Tips

Prepare examples that demonstrate your ability to communicate technical concepts clearly.
Be ready to discuss your approach to model evaluation and selection.
Think about how you create and select features for your models.

Commonly Asked DSA Questions

Frequently asked coding questions at BNY Mellon

View all
Two SumEASYLongest Substring Without Repeating CharactersMEDIUMLongest Palindromic SubstringMEDIUMRoman to IntegerEASY3SumMEDIUMGroup AnagramsMEDIUMBest Time to Buy and Sell StockEASYGas StationMEDIUMMove ZeroesEASYReverse Words in a String IIIEASY