Data Scientist - Director - Data & Analytics Engineering
Morgan StanleyMatch Score
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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand, metro location, and mid-level ML role attract many qualified applicants.
Core ML skills transfer across industries, but fraud and regulated banking experience increases domain specificity.
Explicit 6+ years, deep ML/statistics requirements and Python/Big Data stack make filters stringent.
Job Description
Structured overview of role & requirementsAbout This Role
Own end-to-end development of statistical and machine learning models to detect and mitigate fraud risk across Morgan Stanley products.
Monitor and maintain deployed models, diagnosing performance degradation and recommending recalibration or retirement as needed.
Provide technical guidance and mentoring to junior data scientists and engineers; manage assigned model development projects with senior team support.
Minimum Requirements
6+ years professional experience in data science, machine learning, statistical modeling, or quantitative analytics.
Advanced proficiency in Python and SQL; experience with big data tools such as Hadoop, Hive, Impala, Spark, or PySpark.
Expertise in statistical inference, supervised and unsupervised machine learning methods, feature engineering, and performance evaluation in complex, adversarial environments.
Work Experience Required: 6+ years; Notice Period: Not explicitly mentioned in the JD.
Ideal Candidate Profile
Demonstrated ability to manage full lifecycle of fraud detection model development independently within a regulated financial services environment.
Strong depth in statistical foundations including dealing with class imbalance, overfitting, data leakage, and model stability for non-stationary, adversarial data.
Experience with fraud detection, financial crime or transaction monitoring models leveraging large transactional or behavioral datasets in financial services.