Credit Risk Modeler, Assistant Vice President

Who we are looking for

A strong quantitative modeler to join the team as Assistant Vice President and Credit Risk Modeler based in New Jersey, Connecticut, or Boston. This role is part of the Centralized Modeling, Analytics and Operations Group within Enterprise Risk Management’s Financial Risk Organization.

The team you will be joining plays an important role in the overall success of the organization. Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. To make that happen we need teams like yours to help navigate employees and the organization as a whole. In your role you will strive for cutting‑edge solutions, that are straightforward and scalable.

You will help us build resilience and execute day‑to‑day deliverables at our best. Join us if making your mark in the financial services industry from day one is a challenge you are up for.

As Credit Risk Modeler you will:

• Develop PD/LGD/EL models to support credit risk analytical processes including Basel/CCAR/CECL/IFRS9/Credit Ratings/ICAAP for wholesale portfolio

• Review and enhance credit risk analytical methodology including modeling choices in line with expanding business and regulatory requirements

• Review and verify key model assumptions with model owners

• Review model outputs with properly justified opinions and judgments by experts from credit risk managers to capture forward‑looking financial market and macro‑economic outlooks

• Implement internally developed models on risk analytical library platform

• Streamline the existing modeling and analytical process; increasing the pace of execution to meet the needs of the business

• Work in close partnership with the three lines of defense functions, such as model governance, Corporate Audit and Financial Regulatory Assurance to ensure appropriate governance and control infrastructure for credit risk analytics

• Prepare and present required reports/reviews to model risk management, senior management and global regulators

These skills will help you succeed in this role:

• Strong analytical and quantitative mindset; ability to take ownership and improve on existing risk models and methodologies

• Energetic/motivator: an enthusiastic individual with proven leadership skills and an ability to motivate a diverse, multi‑level workforce and instill a sense of urgency on a range of evolving goals and objectives

• Organizational strengths: an ability to organize projects, processes and priorities to ensure business needs are met in a coordinated, responsive and timely manner, with minimal direction

• Confidence: a self‑assured, experienced and knowledgeable individual able to quickly garner support for his/her views based on informed, well‑presented direction or analysis, with a willingness to negotiate, and concede, when needed

• Communicator: clear, confident, self‑assured communication style, coupled with an ability to react and adapt to various audiences and environments without diluting effectiveness

• PhD in statistics or econometrics or equivalent, prefer research area in survival analysis/event history analyses or related areas;
  Prefer PhD research that involves heavy programming work with strong programming skills in Python/R/C/C++/SQL etc.

• Undergraduate training in mathematics and probability theory (measure theory) with good knowledge of stochastic calculus is a big plus.

• Prefer 5+ years of experiences for MS or 2+ years of experience for PhD (will consider fresh PhD with solid academic background and strong programming skills) of developing credit risk modeling for in a financial institution

• Strong programming skills in Python/R/C/C++/SQL etc.

• Demonstrated experiences working with model development teams, analytical library development team and technology

• Motivated and fascinated in how to apply statistics and econometric methodologies to resolve credit risk modeling challenges in financial industry

$90,000 - $157,500 Annual

The range quoted above applies to the role in the primary location specified. If the candidate…