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# AWM, Marcus by Goldman Sachs, Data Scientist - Fraud Strategy, Analyst - Richardson, TX

- **Company:** Goldman Sachs, Inc.
- **Location:** Richardson, TX, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Microsoft Excel, Artificial Neural Networks, Cluster Analysis, Data Visualization, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, SQL Databases, Unstructured Data, Reinforcement Learning, Snowflake, Deep Learning, Pyspark, Data Management, Databricks, Programming Languages
- **Published:** May 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=180c02f5467e9c6c

## About the Role

* 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, * 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

## Description

* 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


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