Senior Data Scientist

Overview

This role in Fraud Analytics Data Science team is responsible for designing, evaluating, and maintaining advanced analytical and machine learning solutions to detect and prevent Identity Theft fraud. This role focuses on improving fraud detection effectiveness while minimizing false positives, operational friction, and member impact. The position requires strong analytical judgment, endtoend ownership of complex analyses, and close collaboration with crossfunctional partners. The role includes engagement with thirdparty fraud vendors, focused on analytical validation and model performance oversight. This role analyze complex datasets for fraud patterns, and collaborate with cross-functional teams and vendor to enhance fraud detection systems and communicate analytical insights to stakeholders and executive leadership. Principallevel candidates will also be considered.

Responsibilities

• Lead development, evaluation, and enhancement of statistical and machine learning models used for fraud detection.
• Conduct deepdive analyses on fraud trends, largeloss events, false positives, and operational drivers.
• Design and validate feature engineering approaches using transactional, behavioral, and operational data.
• Apply appropriate techniques for highly imbalanced datasets, including costsensitive modeling and anomaly detection.
• Translate analytical findings into clear, actionable recommendations.
• Conduct deep-dive analyses on fraud patterns
• Work with large-scale datasets, ensuring data quality, feature engineering and model interpretability
• Develop algorithms and automate processes in cloud environment
• Use modeling and trend analysis to analyze data
• Collaborate with team members and participate in team projects and initiatives
• Utilize effective written and verbal communication to document and present findings

Qualifications

• 3-5 years of experience in exploratory data analysis and predictive modeling
• Solid foundation in statistics and machine learning techniques
• Experience working with highly imbalanced datasets
• Ability to communicate complex technical findings to non-technical stakeholders
• Strong programming skills in PySpark, Python, SQL
• Technical writing
• Data storytelling and technical presentation skills
• Model Development
• Communication
• Critical Thinking
• Collaborate and Build Relationships
• Master's Degree in Data Science, Statistics, Mathematics, Computers Science, Engineering, or degrees in similar quantitative fields

Desired Qualifications

• PhD's Degree in Data Science, Statistics, Mathematics, Computer Science, or Engineering

Hours: Monday - Friday, 8:00AM - 4:30PM
Location: 820 Follin Lane, Vienna, VA 22180

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