TIAA is seeking a Lead Data Scientist to join our Fraud Data Analytics and AI Strategy team. In this role, you will sit at the intersection of advanced analytics, generative AI development and fraud prevention & detection, working to protect our customers and the institution from ever evolving fraud threats. You will be responsible for deep-dive analysis of fraud data, evaluation and optimization of existing fraud prevention and detection systems, and the development of innovative analytical and generative / agentic AI solutions on the latest technologies. The ideal candidate is a fast learner who thrives in a collaborative environment, can translate complex data findings into actionable business recommendations, and is eager to explore frontiers of agentic and generative AI applications in financial services. You will work closely with fraud operations and technology teams, serving as an analytical bridge between business strategy and technical execution.
Key Responsibilities and Duties
- Analyze large, complex fraud datasets to identify patterns, trends, and anomalies that inform detection strategies and business decisions
- Evaluate existing static, rule-based fraud detection systems through data-driven assessments of their performance and coverage, and deliver clear, prioritized recommendations for rule updates, retirement, or new rule creation
- Partner with fraud operations teams to understand frontline detection challenges and translate operational insights into analytical hypotheses and actionable solutions
- Utilize AI-assisted development tools such as Amazon Q and Kiro to accelerate analytical workflows and solution delivery
- Prototype and contribute to the development of agentic AI applications leveraging AWS AgentCore and generative AI solutions that advance the team's fraud strategy capabilities
- Collaborate with fraud technology teams to ensure models, rules, and AI-driven outputs are implemented accurately and monitored effectively within the AWS production environment
- Design, build, and validate machine learning and statistical models to enhance fraud detection capabilities, improve precision and recall, and reduce false positive rates
- Monitor deployed models and fraud rules on an ongoing basis, identifying performance degradation or emerging detection gaps that require intervention
- Communicate findings, model results, and strategic recommendations clearly to both technical and non-technical stakeholders
- Stay current with emerging trends in fraud typologies, financial crime, and AI and machine learning developments within the AWS ecosystem, and bring relevant innovations back to the team
Educational Requirements
- University (Degree) Preferred
Work Experience
- 5+ Years Required; 7+ Years Preferred
Physical Requirements
- Physical Requirements: Sedentary Work
Career Level
8IC
Required Skills:
- 5+ years of hands-on experience in data science, analytics, or a closely related quantitative discipline
- Strong proficiency in Python or R for statistical analysis and model development
- Solid command of SQL and experience working with large-scale structured and unstructured datasets
- Demonstrated expertise in statistical modeling and machine learning techniques including classification, regression, clustering, and anomaly detection
- Ability to manage relationships across multiple stakeholder groups including operations and technology teams
- Proven ability to learn new domains, tools, and methodologies quickly and independently
- Solid understanding of model evaluation metrics for imbalanced classification problems (e.g., precision, recall, AUC, F1)
Preferred Skills:
Related Skills
Business Acumen, Data Preprocessing, Data Science, Innovation, Machine Learning (ML), Market/Industry Dynamics, Predictive Modeling, Programming, Statistics
Anticipated Posting End Date:
2026-07-30
Base Pay Range: $118,000/yr - $149,000/yr
Actual base salary may vary based upon, but not limited to, relevant experience, time in role, base salary of internal peers, prior performance, business sector, and geographic location. In addition to base salary, the competitive compensation package may include, depending on the role, participation in an incentive program linked to performance (for example, annual discretionary incentive programs, non-annual sales incentive plans, or other non-annual incentive plans).
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