Model Risk Associate: BSA/AML Analytics (Hybrid)

SMBC; Jersey City (NJ); Hybrid; USD 112,778 - 135,000; Full time

Benefits offered by this job

Hybrid work model

Annual discretionary incentive

Job summary

SMBC Group is a leading global financial group with a strong presence in the Americas. This role focuses on validating BSA/AML/Fraud models using SQL, Python/R and Hadoop, ensuring conceptual soundness and robust performance.

The candidate will assess data quality, design of models, and governance frameworks. The position requires a Master’s degree in financial engineering or mathematics and 3 years of relevant experience, with deep data science and machine learning skills.

Qualifications

• Master’s degree in financial engineering, mathematics or a related field plus 3 years of experience.
• Advanced data science and financial data engineering skills with programming in SQL, Python or R.
• Knowledge of model risk management and regulatory guidance SR 11-7.

Responsibilities

• Conduct end-to-end validations and review of BSA/AML/Fraud models using SQL, Python/R and Hadoop.
• Identify model deficiencies, communicate issues and provide feasible recommendations.
• Develop documentation templates, testing packages and automation tools for validation processes.

Skills

Advanced data science & financial data

Statistical testing methodologies

Machine learning techniques

Data segmentation methods

Stress and sensitivity analysis

Model risk management frameworks

Regulatory guidance SR 11-7

Financial services industry knowledge

SQL

Python

R

Hadoop

Education

Master’s degree in financial engineering, mathematics or related field

Tools

SQL

Python

R

Hadoop

Job description

SMBC Group is a leading global financial group with a strong presence in the Americas. This role focuses on validating BSA/AML/Fraud models using SQL, Python/R and Hadoop, ensuring conceptual soundness and robust performance.

The candidate will assess data quality, design of models, and governance frameworks. The position requires a Master’s degree in financial engineering or mathematics and 3 years of relevant experience, with deep data science and machine learning skills.