Quantitative Research – Prime Finance – Vice President

The Prime Finance Quantitative Research (QR) team in London develops and maintains advanced mathematical models, innovative methodologies, and robust infrastructure to support and grow the Prime Financial Services business. Our mission is to optimize decision-making, automate processes, and manage risk.

Prime Financial Services provides financing and securities lending to institutional investors, optimizes the bank's inventory and balance sheet, and delivers strategic solutions to clients. The QR team partners closely with trading, technology, and risk teams to deliver impactful tools and analytics.

The team specializes in building models that leverage Machine Learning, Statistics, and Operations Research to solve complex business challenges. As a VP in QR Prime Finance, you will collaborate with senior stakeholders to design and implement those models and help drive business revenue, enhance risk management, and automate workflows. Typical projects include predicting changes in borrow rates or forecasting market demand, unraveling patterns and causality in the data, and optimizing pricing and inventory allocation to maximize our revenue and profits. 

As Vice President in the team you will be involved in regular collaboration with the trading desk. In addition to strong technical expertise, excellent communication skills are essential for effectively engaging with stakeholders and translating complex quantitative concepts into actionable business solutions.

Experience in Prime Finance is preferred but not required. We provide on-job training, and through the diversity of the businesses it supports and the variety of functions that it is responsible for, the Quantitative Research group provides unique growth opportunities for you to develop your abilities and your career.

Job responsibilities:

• Develop and implement mathematical, statistical, and machine learning models to optimize revenue and profitability for stock borrow-loan, cash, and synthetic financing books.
• Design and deploy predictive analytics to forecast borrow rates, product demand, and other key business drivers.
• Apply operations research and optimization techniques to automate and enhance traders' decision-making and inventory management.
• Analyse market data to uncover patterns, causality and inform business strategy.
• Collaborate with trading, technology, and risk teams to deliver quantitative tools and solutions.
• Build and maintain robust infrastructure for model deployment and analytics delivery.
• Ensure models and analytics meet rigorous control and risk management standards.
• Continuously improve existing models and methodologies in response to evolving business needs.
• Communicate complex quantitative concepts to senior stakeholders and non-technical audiences.
• Mentor junior team members and contribute to a collaborative team environment.
• Document models, methodologies, and processes for transparency and knowledge sharing.

Required Qualifications, Capabilities and Skills

• Advanced degree (Masters or PhD) in Statistics, AI/ML, Computer Science, Operations Research, or related quantitative field.
• Significant experience in quantitative modelling, analytics, or related roles.
• Strong background in AI, machine learning, operations research, or revenue/yield management.
• Proficiency in Python for data analysis, modelling, and software development.
• Deep understanding of statistics, AI, and optimization techniques.
• Demonstrated ability to solve complex quantitative problems and deliver business impact.
• Excellent communication and presentation skills, especially with senior stakeholders.
• Ability to work collaboratively in cross-functional teams.
• Strong organizational skills and attention to detail.
• Experience with large-scale data analysis and visualization tools.

Preferred Qualifications Capabilities and Skills 

• Experience in Prime Finance, securities lending, or inventory management.
• Familiarity with revenue and yield management strategies.
• Hands-on experience with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
• Knowledge of optimization libraries and techniques (e.g., Gurobi, CPLEX, linear/nonlinear programming).
• Track record of publishing research or presenting at industry conferences.
• Experience mentoring or leading junior researchers.