Lead Data Scientist, Fraud Analytics & ML
Overview
Own the fraud detection strategy for a financial services client, reducing fraud losses by 20% within the first year. Lead a team of 5 data scientists and collaborate with engineering to deploy ML models in production. You will work directly with the VP of Risk to align fraud use cases with business goals. This role stands out for its focus on Graph Analytics and AI techniques to uncover hidden fraud rings.
What You'll Do7
- 1Build ML models for real-time fraud detection, achieving <50ms latency in production.
- 2Design Graph Analytics solutions to identify fraud rings, reducing false positives by 15%.
- 3Own the end-to-end fraud model lifecycle from ideation to deployment, using Python and Spark.
- 4Drive feature engineering for fraud models, incorporating NLP for transaction text analysis.
- 5Lead a team of 3 data scientists, mentoring them on model development and MLOps practices.
- 6Ship a fraud detection dashboard using Tableau for real-time monitoring by risk analysts.
- 7Collaborate with data engineers to optimize data pipelines, cutting feature computation time by 30%.
Requirements7
- 15+ years in data science with a focus on Fraud Analytics.
- 22+ years leading data science teams in a production environment.
- 3Expertise in Python and SQL for data manipulation and modeling.
- 4Experience with Spark for large-scale data processing.
- 5Bachelor's degree in Computer Science or related field; Master's preferred.
- 6Track record of deploying fraud detection models with ML algorithms.
- 7Knowledge of Graph Databases (e.g., Neo4j) is a plus.
Salary Insight
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