Senior Data Science Manager: Model Risk & Governance

Egug; New York (NY); On-site; USD 150,000 - 230,000; Full time

Job summary

Egug is seeking a senior professional to oversee model risk management within the MRMG, focusing on credit risk, fraud risk, and line management models. The role includes evaluating models for soundness, data quality, performance, and ongoing monitoring, while communicating findings to executives and regulators.

The ideal candidate will have an advanced quantitative degree, hands-on modeling experience, and deep knowledge of ML algorithms, big data platforms, and programming languages (Python/R,

Qualifications

• Advanced degree in quantitative fields.
• Hands-on modeling experience preferred.
• Strong understanding of machine learning algorithms (e.g., gradient boosting, XGBoost, neural networks) and statistical modeling techniques.
• Experience with large data processing and platforms such as Hadoop, Pig, Hive, Spark, AWS.
• Proficiency in Python/R; Java, Scala, or C++ a plus.
• Strong communication and written skills.
• Flexibility to work under tight deadlines and shifting priorities.

Responsibilities

• Oversee credit risk, fraud risk, and line management models, including evaluating conceptual soundness, data quality, performance validation, and ongoing monitoring.
• Document detailed model review reports and prepare for regulatory and internal audit reviews.
• Communicate model review results to business partners, senior leaders, and regulators.
• Evaluate machine learning algorithms and their application on models.
• Enhance governance framework to accommodate evolving modeling techniques and model risk management.

Skills

Communication skills

ML concepts

Education

Advanced degree in quantitative fields

Tools

Python

R

Java

Scala

C++

Hadoop

Pig

Hive

Spark

AWS

Job description

Egug is seeking a senior professional to oversee model risk management within the MRMG, focusing on credit risk, fraud risk, and line management models. The role includes evaluating models for soundness, data quality, performance, and ongoing monitoring, while communicating findings to executives and regulators.

The ideal candidate will have an advanced quantitative degree, hands-on modeling experience, and deep knowledge of ML algorithms, big data platforms, and programming languages (Python/R,