AWM, Marcus by Goldman Sachs, Data Scientist - Fraud Strategy, Analyst - Richard
Goldman Sachs Group, Inc., Richardson, TX, United States
AWM, Marcus by Goldman Sachs, Data Scientist - Fraud Strategy, Analyst - Richardson, TX
Job Description
As part of this team you will be responsible for:
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 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 evaluating new data sources to build effective fraud controls
Creating trend reports and analysis leveraging coding languages 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 developing 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 datasets using Big Data tools and platforms (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 networks, 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 algorithms)
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
Job Info
Job Identification 164698
Job Category Analyst
Posting Date 03/31/2026, 09:14 PM
Locations Richardson, Texas, United States
Goldman Sachs is an equal‑opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.
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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 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 evaluating new data sources to build effective fraud controls
Creating trend reports and analysis leveraging coding languages 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 developing 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 datasets using Big Data tools and platforms (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 networks, 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 algorithms)
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
Job Info
Job Identification 164698
Job Category Analyst
Posting Date 03/31/2026, 09:14 PM
Locations Richardson, Texas, United States
Goldman Sachs is an equal‑opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.
#J-18808-Ljbffr