Data Scientist - Director - Data & Analytics Engineering
Core
Develop statistical and machine learning models to identify, assess, and mitigate fraud risk across Morgan Stanley products.
Role type
Director-level individual contributor data scientist (fraud detection)
Builds
Production ML models for fraud detection and risk mitigation
Domain
Financial services / Fraud detection
Deliverable
production ML models
Required skills
Statistical inference, probability, sampling, hypothesis testing, regularization, bias-variance trade-offs, optimization, feature selection, dimensionality reduction, model calibration, statistical diagnostics, exploratory data analysis, feature engineering, Python, SQL, Hadoop, Hive, Impala, Spark, PySpark
Preferred skills
Fraud detection, financial crime, transaction monitoring, payment risk, anomalous-behavior detection, high-volume transactional data analysis, fraud typologies, Model Risk Management, Tableau
Responsibilities
Independently execute end-to-end model development, develop and evaluate models for imbalanced and adversarial environments, apply statistical principles to model selection and validation, monitor deployed models and diagnose performance degradation, provide technical guidance and mentoring to junior staff
Seniority
Director, hands-on IC
