AWM, Marcus by Goldman Sachs, Data Scientist - Fraud Strategy, Analyst - Richard
The Goldman Sachs Group, Richardson, TX, United States
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, PySpark, 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
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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, PySpark, 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
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