Lead Data Scientist Gen AI Lead

TIAA is seeking a Lead Data Scientist to join our Enterprise Fraud Management team. The individual will work on designing implementing and evaluating models that detect fraud across a variety of channels. You will also contribute to exploratory projects involving generative AI (e.g. synthetic data generation anomaly scenario simulation language modeling for fraud detection insights). This is a handson role for someone looking to grow in both classical data science and emerging AI technologies.

Key Responsibilities and Duties

• Develop and maintain fraud detection models using supervised and unsupervised learning techniques (e.g. random forests XGBoost isolation forests Clustering Techniques).
• Partner with engineers and analysts to deploy models in production and monitor their performance.
• Perform data wrangling feature engineering and exploratory data analysis (EDA) on largescale transactional and behavioral data.
• Collaborate crossfunctionally with product risk and engineering teams to understand fraud patterns and improve detection coverage.
• Assist in the development of dashboards and visualizations for communicating model insights to stakeholders.
• Use generative AI (e.g. LLMs) to support advanced analytics projects such as:
  • Generating synthetic fraud scenarios
  • Automating risk report generation
  • Detecting or interpreting anomalies in textual/behavioral data

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 experience in a data science or machine learning role

• Proficiency in R and/or Python (pandas scikitlearn numpy matplotlib etc.

• Experience with SQL and working with large datasets

• Familiarity with cloud environments like AWS GCP or Azure

• Solid understanding of model evaluation metrics for imbalanced classification problems (e.g. precision recall AUC F1

Preferred Skills:

• Bachelors or Masters degree in Computer Science Statistics Data Science or related field

• Knowledge of fraud prevention and detection modeling techniques and fraud domain fundamentals

• Experience with MLOps tools or pipelines (e.g. MLflow Airflow Docker)

• Exposure to LLM prompt engineering or finetuning tasks

• Prior work with synthetic data generation or anomaly simulation

• Excellent communication skills & team player

Anticipated Posting End Date:

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 nonannual sales incentive plans or other nonannual incentive plans).

Company Overview

Every worker deserves a secure retirement. For more than 100 years TIAA has delivered it for millions of people. Founded to help educators retire with dignity today were a marketleading retirement company fueled by worldclass asset management. But were not just another legacy financial services firm. Were fighting harder than ever before for our clients and the many Americans who need us.

Benefits and Total Rewards

The organization is committed to making financial wellbeing possible for its clients and is equally committed to the wellbeing of our associates. Thats why we offer a comprehensive Total Rewards package designed to make a positive difference in the lives of our associates and their loved ones. Our benefits include a superior retirement program and highly competitive health wellness and work life offerings that can help you achieve and maintain your best possible physical emotional and financial wellbeing. To learn more about your benefits please review our Benefits Summary.

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We are an Equal Opportunity Employer. TIAA does not discriminate against any candidate or employee on the basis of age race color national origin sex religion veteran status disability sexual orientation gender identity or any other legally protected status.

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