OUR IMPACT

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 consumer business provides digital solutions for customers to better spend, borrow, invest, and save. 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.

• Analyzing large volumes of data leveraging advanced statistical techniques to uncover new fraud pattern, 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 the customer account.
• 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 report and analysis leveraging coding language and tools such as Python, PySpark, SQL, Tableau and Excel
• Synthesize current portfolio risk or trend data to support recommendation 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

• 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 (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 core banking products such as savings, checking, certificate deposit, credit card etc.
• Creativity to go beyond tools and comfort working independently on solutions
• Demonstrated thought leadership, creative thinking and project management Skills

• 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 used data visualization to report on trends and analysis