Senior Data Scientist 2026P-0110
The Senior Data Scientist provides advanced analytical and modeling expertise to support high-impact fraud detection, identity theft analytics, and predictive modeling efforts. This role is engaged selectively for complex modeling challenges, optimization of existing models, and rapid response to emerging fraud schemes that require deep technical expertise.
The ideal candidate is a highly analytical, detail-oriented problem solver who thrives in ambiguous, fast-moving environments. This individual brings strong expertise in machine learning, statistical modeling, and experimentation, and demonstrates the ability to translate complex data into actionable insights. The candidate must be self-directed, capable of anticipating risks and opportunities, and able to develop scalable analytical solutions with minimal oversight.
This role supports government-led fraud analytics by enhancing predictive models, improving detection accuracy, and optimizing analytical frameworks used to identify identity fraud and emerging schemes. Tools and environments may include Python, R, SQL, cloud-based analytics platforms, and large-scale data processing frameworks.
Key Responsibilities / Day-to-Day Activities
- Develop advanced predictive models and machine learning algorithms to detect identity fraud and anomalous filing patterns
- Optimize existing models, filters, and business rules to improve accuracy and reduce false positives
- Analyze large, complex datasets to identify trends, patterns, and emerging fraud schemes
- Conduct experimentation and model validation to assess performance and reliability
- Collaborate with data engineers and analysts to integrate models into production and non-production environments
- Evaluate internal and external data sources to enhance fraud detection capabilities
- Perform statistical analysis to support treatment optimization and fraud classification
- Respond rapidly to emerging fraud threats by developing and deploying analytical solutions
- Document methodologies, assumptions, and model performance metrics for transparency and reproducibility
- Provide technical guidance and recommendations to support government decision-making
How to Apply
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| Job Category | Data Analysis and Analytics |
| MINIMUM QUALIFICATIONS | Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field |6 to 8 years of experience in data science, predictive analytics, or machine learning |
| REQUIRED SKILLS | Strong proficiency in Python and/or R for statistical modeling and machine learning Experience with SQL and working with large structured and unstructured datasets |
| TECHNICAL SKILLS | Demonstrated experience developing, validating, and deploying predictive models,| Knowledge of machine learning techniques such as classification, clustering, regression, and anomaly detection, | Experience with data visualization tools such as Tableau, Power BI, or similar |
| DESIRED QUALIFICATIONS | Experience supporting federal agencies, preferably Treasury or IRS environments |Familiarity with fraud detection, identity theft analytics, or financial crime modeling | Experience with cloud platforms such as AWS, Azure, or Google Cloud |Knowledge of big data frameworks such as Spark or Hadoop |Experience with model governance, explainability, and auditability practices | Advanced degree (PhD preferred) in a quantitative discipline | Familiarity with Agile or iterative development methodologies |
| SUITABILITY REQUIREMENTS | Ability to obtain and maintain a Public Trust (Moderate Risk) clearance | U.S. Citizenship required |