# Data Scientist I
**Role:** Data Scientist · **Level:** E
**Company:** [BNY Mellon](https://scaleengineer.com/companies/bny-mellon)
**Difficulty:** Medium
**Salary:** US$85000 - US$110000
**Experience:** 1 - 3
**Timeline:** ~14 days
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.
Canonical: https://scaleengineer.com/interviews/bny-mellon/e-data-scientist
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## Overall evaluation

- Technical Skills and Knowledge
- Problem Solving and Application
- Communication and Behavioral

## Questions asked

- 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?

## Preparation tips

### lists

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

### studyPlan

- {"title":"Foundational Statistics","longDescription":"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.","shortDescription":"Weeks 1-2: Statistics & Probability (Python libraries)"}
- {"title":"Machine Learning Fundamentals","longDescription":"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.","shortDescription":"Weeks 3-4: Machine Learning Algorithms (Scikit-learn)"}
- {"title":"Python for Data Science","longDescription":"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.","shortDescription":"Weeks 5-6: Python for Data Science (Pandas, NumPy, Visualization)"}
- {"title":"SQL Proficiency","longDescription":"Week 7: Strengthen your SQL skills. Practice writing queries for data extraction, filtering, joining, aggregation, and window functions. Work through common SQL interview problems.","shortDescription":"Week 7: SQL for Data Analysis"}
- {"title":"Project and Behavioral Preparation","longDescription":"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.","shortDescription":"Weeks 8-9: Behavioral & Project Deep Dive (STAR Method)"}
- {"title":"Final Preparation","longDescription":"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.","shortDescription":"Week 10: Mock Interviews & Final Review"}

## Location differences

- {"location":"New York","differences":{"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."],"interviewFocus":["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."],"commonQuestions":["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."]}}
- {"location":"London","differences":{"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."],"interviewFocus":["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."],"commonQuestions":["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?"]}}