Asset & Wealth Management, Data Scientist - Fraud Strategy, Associate - Richardson, TX
Goldman Sachs · Richardson, TX
- On site
- Full-time
- $120,000 / year
- Richardson, TX
Job highlights
- Analyze data for new fraud patterns.
- Develop data-driven fraud strategies.
- Use machine learning for risk identification.
- Build data features for fraud models.
- Collaborate on new data-driven solutions.
About the role
Data Scientist - Fraud Strategy Associate
Goldman Sachs' Asset & Wealth Management (AWM) offers a unique opportunity within a premier global financial institution. We support a diverse clientele, including mutual funds, hedge funds, pension plans, sovereign wealth funds, insurance companies, endowments, foundations, third-party wealth firms, and ultra-high-net-worth individuals. With over $3 trillion in assets under supervision, AWM provides innovative investment and advisory services across traditional and alternative investments, prioritizing long-term performance and client success.
Marcus by Goldman Sachs
Marcus, the digital consumer banking arm of Goldman Sachs, offers high-yield savings accounts and Certificates of Deposit directly to consumers. Leveraging 150+ years of Goldman Sachs expertise with intuitive digital experiences, Marcus focuses on value, transparency, and simplicity for its millions of customers, operating as the largest pure online bank without physical branches.
Responsibilities:
- Analyze extensive data using advanced statistical techniques to identify new fraud patterns and conduct in-depth reviews.
- Design and develop data-driven fraud strategies and capabilities for consumer money movement products to mitigate fraud losses.
- Utilize supervised and unsupervised machine learning to accurately detect high-risk activities on customer accounts.
- Build new data features and products to enhance statistical fraud models.
- Identify data signals to effectively differentiate between fraudulent and non-fraudulent activities.
- Explore and integrate new data sources for robust fraud control development.
- Generate trend reports and analyses using Python, PySpark, SQL, Snowflake, Databricks, and Excel.
- Synthesize current portfolio risk and trend data to inform recommendations.
- Investigate and leverage cloud-based data science technologies to advance fraud controls.
- Measure and monitor the impact of risk controls on customers and develop strategies for a positive customer experience.
- Collaborate with technology and capability partners to implement innovative data-driven solutions.
Basic Qualifications
- Bachelor’s degree in Mathematics, Statistics, Economics, Finance, Engineering, or a related field.
- Proven experience with large datasets using Big Data tools and platforms (e.g., Python, PySpark, Snowflake, Databricks, SQL).
- Ability to derive key insights and signals from complex structured and unstructured data.
- Strong working knowledge of statistical techniques including regression, clustering, neural networks, and ensemble methods.
- 2+ years of experience in fraud risk management, preferably with banking products (savings, checking, CDs, credit cards).
- Creativity in developing solutions beyond standard tools and comfort working independently.
- 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 consumer checking and savings businesses.
- Familiarity with large-scale graph processing, including graph clustering and link prediction algorithms.
- Expertise in advanced machine learning techniques (ensemble methods, reinforcement learning, deep neural networks).
- Knowledge of fraud risk vendors and technologies in consumer finance or digital services.
- Experience with consumer banking authentication tools and methodologies.
- Experience in reporting and data visualization tools for trend analysis.
Key skills/competency
- Data Science
- Fraud Strategy
- Machine Learning
- Python
- PySpark
- SQL
- Snowflake
- Databricks
- Statistical Analysis
- Risk Management
Skills & topics
- Data Scientist
- Fraud Detection
- Machine Learning
- Python
- PySpark
- SQL
- Data Analysis
- Risk Management
- Asset Management
- Wealth Management
- Big Data
- Statistical Modeling
- Consumer Banking
- Associate
How to get hired
- Tailor your resume: Highlight your experience with large datasets, Big Data tools (Python, PySpark, SQL), and fraud risk management.
- Showcase your skills: Emphasize your proficiency in statistical techniques and machine learning for fraud detection.
- Demonstrate impact: Quantify your achievements in previous fraud risk roles, focusing on measurable results.
- Prepare for technical questions: Be ready to discuss your approach to analyzing complex data and building predictive models.
- Understand the business: Research Goldman Sachs' AWM and Marcus by Goldman Sachs to articulate your fit.
Technical preparation
Master Python for data manipulation and analysis.,Practice SQL for querying large databases.,Build models with Scikit-learn or TensorFlow.,Familiarize with PySpark for big data processing.
Behavioral questions
Describe a complex data problem solved.,How do you handle ambiguous project requirements?,Share an example of influencing stakeholders.,Discuss a time you worked with large datasets.
Frequently asked questions
- What specific fraud patterns will a Data Scientist focus on at Marcus by Goldman Sachs?
- As a Data Scientist at Marcus by Goldman Sachs, you will focus on identifying and analyzing emerging fraud patterns within consumer money movement products. This includes detecting suspicious activities on customer accounts, distinguishing between genuine and fraudulent transactions, and evaluating new data sources to strengthen fraud controls.
- What are the key machine learning techniques used for fraud detection at Goldman Sachs?
- Goldman Sachs utilizes both supervised and unsupervised machine learning techniques for fraud detection. This includes regression, clustering, neural networks, and ensemble methods. Advanced techniques like reinforcement learning and deep neural networks are also valuable.
- How important is experience with Big Data tools for this Data Scientist role?
- Experience with Big Data tools and platforms such as Python, PySpark, SQL, Snowflake, and Databricks is essential for this role. You will be working with very large datasets to uncover insights and build robust fraud models.
- What is the typical career progression for a Data Scientist in Asset & Wealth Management at Goldman Sachs?
- Career progression typically involves moving from an Associate role to a Vice President, then to Senior Vice President, and potentially to the Managing Director level. This progression is based on performance, impact, and demonstrated leadership in developing and implementing data-driven strategies.
- What kind of customer experience considerations are involved in fraud risk management at Marcus?
- Ensuring a positive customer experience is crucial. This involves accurately identifying fraud while minimizing false positives that might inconvenience legitimate customers. You will measure the impact of controls and develop strategies to maintain customer trust and satisfaction.
- Does Goldman Sachs encourage independent work for its Data Scientists?
- Yes, Goldman Sachs values creativity and encourages independent problem-solving. While collaboration is key, you'll have the opportunity to work independently on solutions and demonstrate thought leadership.