Data Scientist - Director - Data & Analytics Engineering
Morgan Stanley (company site, opens in a new tab)6+ yrsBengaluruOn-siteDirect career pageEst. 35–60 LPA
Posted on 21 Sept
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Job description
Structured overview of role & requirements
About 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.