Wealth Management, Marcus by Goldman Sachs, Fraud Strategy Analyst - Richardson,

Goldman Sachs - Richardson, TX

Full-time - Mid Level

Richardson, TX

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

About the position

As part of the fraud strategy team at Goldman Sachs, you will play a crucial role in developing innovative strategies, business processes, and solutions aimed at preventing fraud and protecting the firm from financial losses. This position requires collaboration with various teams, including product, technology, operations, and data science, to create effective solutions and enhance the performance of our portfolio. Your work will directly impact our ability to safeguard consumers and the firm against fraudulent activities. In this role, you will analyze large volumes of data using advanced statistical techniques to identify new fraud patterns and conduct in-depth qualitative and quantitative reviews. You will design and develop data-driven fraud strategies that control losses for consumer-centric money movement products. Utilizing both supervised and unsupervised machine learning techniques, you will accurately identify high-risk activities on customer accounts and build new features and data products to improve statistical fraud models. Your responsibilities will also include identifying data signals that differentiate between fraudulent and legitimate financial activities, evaluating new data sources for effective fraud control, and creating trend reports using coding languages and tools such as Python, PySpark, SQL, Tableau, and Excel. You will synthesize current portfolio risk data to support actionable recommendations and explore cloud-based data science technologies to enhance existing fraud controls. Additionally, you will measure and monitor the impact of designed risk controls on customers, ensuring a positive customer experience while working closely with technology and capability partners to implement new data-driven ideas and solutions.

Responsibilities

• Analyzing large volumes of data leveraging advanced statistical techniques to uncover new fraud patterns and perform deep qualitative and quantitative expert reviews.
• Design and develop data-driven fraud strategies and capabilities to control fraud losses for consumer-centric money movement products.
• Leverage supervised and unsupervised machine learning techniques to accurately identify high-risk activities on customer accounts.
• Build new features and data products to improve statistical fraud models.
• Identify data signals to accurately distinguish between fraud and non-fraud financial and account-related activities.
• Identify and evaluate new data sources to build effective fraud control.
• Create trend reports and analysis leveraging coding language and tools such as Python, PySpark, SQL, Tableau, and Excel.
• Synthesize current portfolio risk or trend data to support recommendations for action.
• Explore and leverage cloud-based data science technologies to further enhance existing fraud controls.
• Measure and monitor the impact of designed risk controls on customers, and develop strategies to ensure a positive customer experience.
• Work 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 network, and ensemble techniques.
• 2+ years of experience in fraud risk management for core banking products such as savings, checking, certificate deposits, 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 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.
• Experience with consumer banking authentication tools and methodologies.
• Experience in reporting and using data visualization to report on trends and analysis.

Benefits

• Diversity and inclusion programs
• Training and development opportunities
• Wellness and personal finance offerings
• Mindfulness programs