About the division

Asset & Wealth Management (AWM) offers an unparalleled opportunity at one of the world's leading financial institutions. We are committed to helping a diverse global client base—including mutual funds, hedge funds, pension plans, sovereign wealth funds, insurance companies, endowments, foundations, third-party wealth firms, and ultra-high-net-worth individuals—achieve their financial goals through strategic investment and advisory services. With over $3 trillion in assets under supervision, AWM delivers innovative solutions across traditional public investing and alternative investments, with a focus on long-term performance and client success.

Wealth Management:

Across Wealth Management, Goldman Sachs helps empower clients and customers around the world to 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 direct-to-consumer business provides digital solutions that help customers save and invest. Across Wealth Management, our growth is driven by a relentless focus on our people, our clients and customers, and leading-edge technology, data, and design.

As part of this team you will be responsible for:

• Analyzing large volumes of data leveraging advanced statistical techniques to uncover new fraud pattern, 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, Py Spark, 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

Basic Qualifications:

• 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

Preferred Qualifications:

• 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