Goldman Sachs Wealth Management Marcus Goldman Sachs Fraud 

United States, Texas, Richardson 

813648781

Responsibilities:

• 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

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 (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

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 used data visualization to report on trends and analysis

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