Job Description

Position Description:

Develops financial models with programming languages (Python and R). Identifies all firm-wide models and meta-data to guarantee a standard and challenge risk ratings. Maintains comprehensive, centralized, and standardized model documentation, inventory, and performance using Key Risk Indicators and Reporting Metrics. Develops and implements a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software. Applies data mining, data modeling, Natural Language Processing (NLP), and Machine Learning (ML) to extract and analyze information from large structured and unstructured datasets. Visualizes, interprets, and reports data findings.

Primary Responsibilities:

Implements and executes Model Risk Management (MRM) Framework for quantitative and qualitative models across the model lifecycle.

Works with model developers and owners to ensure all models used by associates and customers are trusted and of the highest quality to drive solid business decisions and results.

Collaborates with business and technology partners across Legal, Risk, Audit, and Compliance to apply methods to validate models and to identify and mitigate potential risks that may result from implementation errors, inaccurate assumptions, data quality challenges, and/or misused models.

Establishes effective standards, policies, and procedures.

Provides complete and accurate documentation.

Performs on-going monitoring of the MRM processes to ensure solid governance and characterizes and quantifies model risk within the model inventory.

Collaborates with the team to validate models across the business, implement model inventory and controlling standards, and verify adherence to the Model Risk Policy.

Collaborates with the team in the development and implementation of the Model Risk Management Framework by enforcing the policies and procedures.

Develops partnerships with Risk Leads and/or model owners and developers.

Interacts with senior leadership and collaborates with colleagues across the enterprise.

Education and Experience:

Bachelor’s degree (or foreign education equivalent) in Finance, Financial Engineering, Financial Mathematics, Applied Mathematics and Computational Science, Accounting, Economics, or a closely related field and three (3) years of experience as a Manager, Quantitative Risk Analysis (or closely related occupation) managing risks associated with quantitative and qualitative modeling, valuation and pricing, investment products, financial planning, and machine learning using Statistical software packages (SQL and R, Python or MATLAB).

Or, alternatively, Master’s degree (or foreign education equivalent) in Finance, Financial Engineering, Financial Mathematics, Applied Mathematics and Computational Science, Accounting, Economics, or a closely related field and one (1) year of experience as a Manager, Quantitative Risk Analysis (or closely related occupation) managing risks associated with quantitative and qualitative modeling, valuation and pricing, investment products, financial planning, and machine learning using Statistical software packages (SQL and R, Python or MATLAB).

Skills and Knowledge:

Candidate must also possess:

Demonstrated Expertise ("DE") performing quantitative and qualitative analyses to validate financial, statistical, and AI models using Python, R, and SQL; developing and implementing mathematical methodologies, algorithms, and diagnostic using Gurobi and LightGBM or scikit-learn (for testing model stability, reliability, performance, and quality control of modelling data).

DE implementing a comprehensive model risk management framework, policies, and procedures to effectively identify, access, and mitigate enterprise-wide model risk; conducting back, stress, and sensitivity testing and scenario analysis to assess model robustness under different conditions, and creating insightful visual representations using NumPy, SciPy and Pandas.

DE performing financial product development and integration validation using product lifecycle management tools (Archer) to ensure seamless integration and rigorous processes; and validating financial products for compliant financial solutions using Refinitiv Eikon, Insomnia, and PostgreSQL or Snowflake to ensure alignment with risk management regulations.

DE developing written and oral presentations using PowerPoint to deliver recommendations to business stakeholders, communicate complex quantitative findings, present feedback on methodologies, and describe and document edge cases.