Lead Data Science Manager
About the Role:
* The ideal candidate will lead a team of data scientists to develop and implement data-driven solutions to manage fraud and loss metrics for assigned product lines or markets.
* This role requires collaboration with cross-functional teams to design and iterate on fraud-prevention frameworks that optimize transaction declines and minimize customer friction.
* The selected individual will guide the team in data preparation, feature engineering, model training, and validation using Python/R and cloud-native platforms (BigQuery, Snowflake).
* Regular syncs with Business Units, Risk Operations, and Compliance are necessary to ensure models and business rules remain aligned with evolving policy and regulatory requirements.
* The Lead Data Science Manager will track model health (drift, false-positive/false-negative rates) and lead root-cause analyses for any anomalies or spikes in loss.
Key Responsibilities:
* Manage a team of 3-6 data scientists in a fraud-risk or financial-services environment.
* Expertise in exploratory data analysis and preparing clean, structured datasets for modeling.
* Familiarity with production ML frameworks (scikit-learn, TensorFlow, PyTorch) and cloud data platforms (BigQuery, Snowflake).
* A deep understanding of fraud-risk principles, including AML/KYC, regulatory compliance, and performance metrics (Precision, Recall, ROC-AUC).
* Strong partnership skills with engineers, product managers, and business leaders.
* Excellent verbal and written communication skills to distill complex findings into clear narratives for both technical and non-technical audiences.
About Us:
We're a forward-thinking organization that's passionate about inventing new approaches to big, ambiguous problems. We're committed to staying ahead of evolving threat vectors and building novel solutions that drive business success.