AVP​/VP ML Researcher Jennings

Overview

This role offers the chance to help establish a new machine‑learning capability within a major global markets environment. The team operates with the mindset of an early‑stage venture: fast, highly collaborative, and centred on delivering measurable value. You will work directly with trading, distribution, and quantitative partners to design, build, and ope rationalise advanced models that strengthen decision‑making, uncover market insights, and improve client outcomes.

The function you join is being built from the ground up, offering genuine ownership, the ability to influence direction, and the opportunity to shape modelling standards, engineering practices, and long‑term research themes. The ideal candidate enjoys solving complex, high‑stakes problems with solutions that are technically strong and commercially relevant.

• Machine Learning Research & Model Development – Create, test, and implement machine‑learning models that support trading, pricing, and risk‑related use cases across multiple asset classes.
• Utilize methods from statistics, applied mathematics, time‑series modelling, forecasting, optimisation, deep learning, and representation learning.
• Prototype solutions rapidly and progress them to production‑grade tools suitable for real‑time or near‑real‑time environments.
• Build models using Python and industry‑standard libraries such as PyTorch or similar frameworks, applying best practices in data handling, performance tuning, and evaluation.
• Scale and deploy models on cloud platforms (e.g., AWS compute environments, ML orchestration frameworks, and managed training services).

• Offer quantitative expertise to improve market understanding, support risk analysis, and enhance trading strategies.
• Conduct detailed research into market behaviour, pricing dynamics, volatility patterns, and other financial phenomena to guide the design of robust modelling frameworks.
• Use exploratory data analysis, simulation, and statistical techniques to validate hypotheses and stress‑test model assumptions.

• Work closely with trading, sales, quants, and technology teams to understand business needs and translate them into technical requirements.
• Collaborate with distribution partners to tailor analytical tools or model‑driven insights for client interactions.
• Partner with engineering and controls groups to ensure models meet standards for governance, monitoring, interpretability, and operational resilience.

• Contribute to the development and upkeep of analytical libraries, toolkits, and front‑office decision‑support frameworks.
• Help evolve shared modelling infrastructure to improve reliability, scalability, reproducibility, and auditability.
• Evaluate emerging machine‑learning techniques, software frameworks, and cloud capabilities and propose adoption where beneficial.

• Lead or contribute to long‑horizon research initiatives involving new model classes, novel data sources, or alternative modelling approaches such as generative methods, reinforcement learning, or advanced NLP.
• Keep up with academic and industry developments and translate cutting‑edge research into practical, production‑ready applications.
• Investigate areas such as embeddings, vector search, retrieval‑augmented modelling, or LLM‑based workflow augmentation where relevant.

• Strategic Ownership – Shape or influence the roadmap for modelling, research themes, infrastructure design, governance, and long‑term team direction.
• People Leadership (if managing a team) – Define roles and responsibilities, develop colleagues' skills, support career progression, provide coaching, and role‑model behaviours that build trust and collaboration.
• Technical Leadership (IC pathway) – Act as a subject‑matter expert, lead long‑term technical initiatives, mentor peers, and integrate expertise across functions.
• Risk & Controls – Identify, assess, and mitigate model‑related risks; strengthen standards for testing, documentation, and governance.
• Stakeholder Engagement – Maintain strong relationships with senior…