Scale Engineer

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·~14d

Experience·1 - 3 yrs

Comp band·US$85000 - US$110000

Evaluation

What they measure.

  • 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.

Preparation

How to prepare.

Tips

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

Study plan

Fig · Study plan — 06 phases

01 / 06
01

Phase 01 of 06

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.

Questions

Commonly 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?

Locations

Regional differences.

Fig · Regions — 02 locations

01 / 02

Location

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.

DSA

Coding questions at BNY Mellon.

Frequently reported on BNY Mellon loops

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