Asset & Wealth Management, Data Scientist - Fraud Strategy, Associate - Richardson, TX

Goldman Sachs•Richardson, TX

3d•Onsite

About The Position

The Asset & Wealth Management (AWM) division offers a unique opportunity within a premier financial institution, dedicated to assisting a diverse global clientele in achieving their financial objectives through strategic investment and advisory services. With over $3 trillion in assets under supervision, AWM provides innovative solutions in both traditional public and alternative investments, prioritizing long-term performance and client success. Marcus by Goldman Sachs, the digital consumer banking arm, offers high-yield savings accounts and Certificates of Deposit directly to individuals, combining Goldman Sachs' extensive expertise with user-friendly digital experiences focused on value, transparency, and simplicity. As a recognized leader in the online banking space, Marcus provides a fully digital experience without physical branches.

Requirements

Bachelor’s degree in Mathematics, Statistics, Economics, Finance, Engineering or a related field.
Proven experience with very large dataset using Big Data tools and platform (e.g., Python, Pyspark, Snowflake, Databricks, SQL)
Ability to efficiently derive key insights and signals from complex structured and unstructured data
Strong working knowledge of statistical techniques including regression, clustering, neural network and ensemble techniques
2+ years of experience in fraud risk management, preferably in banking products such as savings, checking, certificate deposit, credit cards, etc.
Creativity to go beyond tools and comfort working independently on solutions
Demonstrated thought leadership, creative thinking and project management Skills

Nice To Haves

Master’s degree in Mathematics, Statistics, Economics, Finance, Engineering or a related field
Experience building quantitative data driven statistical strategies for a consumer checking and saving business
Familiarity with large-scale graph processing e.g. graph clustering and link prediction mathematical algorithm
Expertise in advanced machine learning techniques – ensemble techniques, reinforcement learning, deep neural network
Knowledge of fraud risk vendors and technology in consumer finance or digital services industry
Experience with consumer banking authentication tools and methodologies
Experience in reporting and data visualization tools to report on trends and analysis

Responsibilities

Analyzing large volumes of data leveraging advanced statistical techniques to uncover new fraud patterns, and perform deep qualitative and quantitative expert reviews
Designing and developing data driven fraud strategies and capabilities to control fraud losses for consumer centric money movement products
Leveraging supervised and unsupervised machine learning techniques to accurately identify high risk activities on the customer account
Building new data features and data products to improve statistical fraud models
Identifying data signals to accurately distinguish between fraud and non-fraud activities
Identifying and evaluate new data sources to build effective fraud controls
Creating trend reports and analysis leveraging coding language and tools such as Python, PySpark, SQL, Snowflake, Databricks and Excel
Synthesizing current portfolio risk or trend data to support recommendation for action
Exploring and leveraging cloud based data science technologies to further enhance existing fraud controls
Measuring and monitoring the impact of designed risk controls on customers, and develop strategies to ensure a positive customer experience
Working closely with technology and capability partners to implement new data driven ideas and solutions