AWM, Marcus, Fraud Strategy, Associate, Dallas

Goldman Sachs - Richardson, TX

Full-time - Mid Level

Richardson, TX

5,001-10,000 employees

Securities, Commodity Contracts, and Other Financial Investments and Related Activities

About the position

The Fraud Strategy Associate at Goldman Sachs plays a crucial role in developing strategies and solutions to prevent fraud and protect the firm from financial losses. This position involves collaboration with various teams, including product, technology, operations, and data science, to enhance fraud detection and prevention capabilities, ensuring a positive customer experience while managing risk.

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 customer accounts.
• Building new features and data products to improve statistical fraud models.
• Identifying data signals to accurately distinguish between fraud and non-fraud financial and account-related activities.
• Identifying and evaluating new data sources to build effective fraud control.
• Creating trend reports and analysis leveraging coding languages and tools such as Python, PySpark, SQL, Tableau, and Excel.
• Synthesizing current portfolio risk or trend data to support recommendations 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 developing strategies to ensure a positive customer experience.
• Working closely with technology and capability partners to implement new data-driven ideas and solutions.

Requirements

• Bachelor's degree in Mathematics, Statistics, Economics, Finance, Engineering or a related field.
• Proven experience with very large datasets using Big Data tools and platforms (Hadoop, Pig, Hive, Python, Pyspark).
• Ability to efficiently derive key insights and signals from complex structured and unstructured data.
• Strong working knowledge of statistical techniques including regression, clustering, neural networks, and ensemble techniques.
• 3+ years of experience in fraud risk management for core banking products such as savings, checking, certificate deposits, and credit cards.
• 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 algorithms.
• Expertise in advanced machine learning techniques - ensemble techniques, reinforcement learning, deep neural networks.
• Knowledge of fraud risk vendors and technology in the consumer finance or digital services industry.
• 5+ years of experience in fraud risk management for core banking products such as savings, checking, certificate deposits, and credit cards.
• Experience with consumer banking authentication tools and methodologies.
• Experience in reporting and using data visualization to report on trends and analysis.

Benefits

• Competitive salary
• Comprehensive health insurance
• Retirement savings plan
• Paid time off
• Professional development opportunities