CWM - Marcus by Goldman Sachs - Head of Data Science - Personal Loans, VP - Wilmington, DE/New York, NY

Location(s) US-NY-New York
Job ID
Schedule Type
Full Time
Vice President
Consumer Banking
Consumer and Wealth Management
Business Unit
DigFin Risk,Fraud,Collections
Employment Type


Consumer and Wealth Management (CWM)

Across Consumer and Wealth Management (CWM), Goldman Sachs helps empower clients and customers around the world reach their financial goals. Our advisor-led wealth management businesses provide financial planning, investment management, banking and comprehensive advice to a wide range of clients, including ultra-high net worth and high net worth individuals, as well as family offices, foundations and endowments, and corporations and their employees. Our consumer business provides digital solutions for consumers to better spend, borrow, invest, and save. Across CWM, our growth is driven by a relentless focus on our people, our clients and leading-edge technology, data and design.



The firm’s Consumer business, Marcus by Goldman Sachs, combines the entrepreneurial spirit of a startup with more than 150 years of experience. Today, we serve millions of customers across multiple products including lending, deposits, financial tools, and our partnership with Apple on Apple Card. We use innovative design, data, engineering and other core capabilities to provide customers with powerful tools and products that are grounded in value, transparency and simplicity. As we build a leading digital consumer bank and expand into new products and partnerships, we are looking for leaders and individual contributors to join our team.




The individual will be responsible to lead a group of highly talented data scientists to support Personal Loan business of Marcus. The individual should have the experience and domain knowledge to lead and establish the best in class data science team in the industry. The role would require to support all verticals that cover Customer life cycle from marketing, underwriting, customer engagement to collection. This would include customer segmentation methodologies, marketing targeting, digital optimization, underwriting and pricing models, portfolio risk models and collection models.

  • The individual will play a significant role in maintaining strong relationship across various vertical owners and maintain an environment of highly engaging and collaborative relationship between the teams
  • The individual is also expected to manage strong relationship with Model Governance, Fair Lending Compliance and legal and second line credit risk functions
  • Develop and train data science resources across geographies
  • The individual will help in carrying out data processing and analysis including statistical analysis, dimensionality reduction, variable selection, custom attribute engineering, etc.
  • The individual will help with the design, development, evaluation and monitoring of predictive models using advanced algorithms
  • The individual will leverage methods from diverse disciplines like machine learning, statistical modelling, information theory, information retrieval and other areas to gain customer insights, draw conclusions and work with business partners to put those insights into action.
  • The individual will work closely with business partners, technology and customer analytics teams to evaluate new and alternate data sources as well as computing paradigms and analytical tools.
  • The individual will participate in data architecture decisions and partner with technology teams to implement models/algorithms in production systems.
  • The individual will help document model assumptions, methodologies, as well as carry out validation and testing to facilitate peer reviews and independent model validation. Also establish automated model performance tracking.

Basic Qualifications

  • Advanced degree (PhD or Masters) in quantitative areas like Engineering, Computer Science, Applied Math, Statistics, or related disciplines.
  • Hands-on experience with multivariate analysis, statistical modeling, information theory, machine learning, clustering, and/or dimensionality reduction techniques in financial services, customer analytics, digital marketing, or similar domains.
  • 10-12 years of prior model development experience in verticals covering credit risk, marketing and/or collections is required.
  • 5-7 years of experience in Managing Teams
  • Experience with one or more modeling tools (R, Matlab, Octave, etc.) and one or more mainstream programming language (Python, Java, C, Scala, C, C++, etc.).
  • Ability to explain complex statistical models and analysis to drive business ideas.

Preferred Qualifications

  • Familiarity with advanced ML models - neural networks (feed forward, CNNs, RNNs, LSTM), Hidden Markov Models, random forests, SVMs, multivariate analysis, clustering, dimensionality reduction or participation in Kaggle data science competitions
  • Experience working with large data sets and Big Data tools and stack (e.g., Hadoop, Spark, etc.).


At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.

We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at

We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more:

© The Goldman Sachs Group, Inc., 2021. All rights reserved.
Goldman Sachs is an equal employment/affirmative action employer Female/Minority/Disability/Veteran/Sexual Orientation/Gender Identity