Quantitative Risk Director, Vice President

Who we are looking for

An experienced professional with a strong technical and quantitative aptitude to act as Quantitative Risk Director, Vice President. This role will report to senior model validation lead, within Model Risk Management Department.

Why this role is important to us

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.

What you will be responsible for

As Quantitative Risk, VP you will

• Lead a team under the supervision of senior model validation lead to conduct model validation activities

• Develop and execute robust validation framework for AI/ML models, including generative AI, nature language processing and deep learning models

• Understand regulatory requirement and keep abreast of recent developments in academic research, industry practice and regulatory expectations as related to AI/ML and AI/ML Risk

• Streamline the existing validation and analytical process and improve execution efficiency to meet business need

• Perform deep analysis on large scale datasets through machine learning approaches.

• Conduct quantitative analysis to evaluate and quantify risks of various models.

• Communicate with model developers and business to relay the issues and feedback and capture the action plans.

• Prepare and present required reports/reviews to management and regulators.

What we value

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 resources, 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

• Talent manager: an ability to quickly assess strengths and weaknesses of team members at all levels, and ability to identify developing talent and nurture accordingly.

Education & Preferred Qualifications

• Master or PHD degree in a quantitative disciplines (e.g. Finance, Statistics, Economics, Mathematics, Computer Science / Engineering, software engineering or equivalents). PhD is preferred

• 5+ years of experience in quantitative analytics including model development or independent model validation in a regulatory financial institution

• Demonstrated modeling and analytical process engineering capabilities.

• Demonstrated experience working with Global Regulators in the banking or financial services sectors; proficient understanding of various regulations

• In-depth knowledge of AI regulatory requirements (e.g., NIST AI Risk Management Framework, etc.).

• Deep understanding of traditional machine learning, generative AI, deep learning and large language models

• Proven track record in AI model validation or model development, including experience in assessing model performance, robustness, bias, explainability, etc.

• Highly proficient coding skills (Python preferred) and familiarity with ML libraries (e.g., TensorFlow, PyTorch)

• Proven interpersonal and communication skills. Demonstrated ability to understand, visualize and communicate quantitative results to non-quant subject matter expert audiences; Proven experience leading a group of quantitative analysts with a moderate to large scope of responsibility

Are you the right candidate? Yes!