Lead Data Scientist – Gen AI Lead

TIAA

Lead Data Scientist working at TIAA on fraud data analytics and AI strategy. Analyzing complex datasets and developing innovative AI applications for fraud detection.

Posted 7/22/2026full-timeDallas • New Jersey, North Carolina, Texas • 🇺🇸 United StatesSenior💰 $118,000 - $149,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in analyzing complex fraud datasets and developing machine learning models to enhance fraud detection capabilities. Proficient in utilizing AI-assisted tools and collaborating with cross-functional teams to implement effective fraud strategies.

Highest-signal resume keywords

Python ProficiencySQL ExpertiseStatistical ModelingMachine Learning TechniquesData Analysis Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ScienceStatistical AnalysisModel DevelopmentAnomaly DetectionClassification TechniquesRegression TechniquesClustering TechniquesModel Evaluation MetricsFraud DetectionData-Driven Assessments

Soft Skills

Stakeholder ManagementCommunication SkillsAnalytical ThinkingProblem SolvingCollaboration

Tools & Technologies

Amazon QKiroAWS AgentCoreAI-Assisted Development ToolsAWS Production Environment

Industry Keywords

Fraud DetectionFinancial CrimeEmerging TrendsAI DevelopmentsMachine Learning

Tech Stack

Tools & technologies

AWSPythonSQL

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

University (Degree) Preferred
5+ Years Required; 7+ Years Preferred
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)

Benefits

Comp & perks

Health insurance
retirement plans
paid time off
flexible work arrangements
professional development opportunities