AVP/VP ML Researcher

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 operationalise advanced models that strengthen decision‑making, uncover market insights, and improve client outcomes.

The function you join is being built from the ground up. That means 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.

Key Responsibilities

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.
• Use advanced methods from statistics, applied mathematics, time‑series modelling, forecasting, optimisation, deep learning, and representation learning.
• Prototype solutions rapidly and progress them to hardened, 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).

Strategic & Analytical Contribution

• 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.

Cross‑Functional Partnership

• 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.

Infrastructure & Tools

• 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.

Innovation & Research Leadership

• 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.

Leadership & Vice President-Level Expectations

Depending on level and career track (people‑leadership or technical‑expert route), responsibilities may include:

Strategic Ownership

• Shape or influence the roadmap for modelling, research themes, infrastructure design, governance, and long‑term team direction.
• Recommend changes to processes, controls, policies, and resource allocation to ensure the team delivers effectively.

People Leadership (if managing a team)

• Define roles and responsibilities, develop colleagues' skills, and support career progression.
• Provide coaching, feedback, and guidance grounded in clear expectations and structured development plans.
• Role‑model behaviours that build trust, encourage collaboration, and promote a high‑performing culture.

Technical Leadership (individual‑contributor specialist pathway)

• Act as a subject‑matter expert for advanced modelling techniques, data architectures, and quantitative approaches.
• Lead complex technical initiatives that span multiple years or business areas.
• Mentor less‑experienced team members on modelling concepts, scientific rigor, coding practices, and analytical reasoning.
• Guide project direction and integrate expertise from other functions to solve multidimensional problems.

Risk & Controls

• Identify, assess, and mitigate model‑related risks, including data drift, model decay, overfitting, and operational failure modes.
• Strengthen standards for testing, documentation, and governance so that all analytical tools meet regulatory and internal‑control requirements.
• Cultivate a culture of transparency, accountability, and responsible model use.

Stakeholder Engagement

• Maintain strong working relationships with senior stakeholders across trading, risk, technology, operations, and control functions.
• Communicate complex technical ideas clearly to both technical and non‑technical audiences.
• Influence decision‑makers through strong reasoning, evidence‑based recommendations, and articulating model benefits and limitations.

Required Skills & Experience

• A postgraduate degree (Master's minimum; PhD preferred) in a STEM‑related discipline such as mathematics, physics, computer science, engineering, or statistics.
• Strong grounding in machine learning, statistical methods, software engineering principles, and algorithmic problem‑solving.
• Demonstrated experience creating solutions that are both scientifically rigorous and practical to deploy in demanding environments.
• Proficiency in Python and core ML/AI libraries, with clean, idiomatic code and good awareness of complexity, performance, and testing.
• Hands‑on experience with cloud‑based model development, training, and deployment workflows.
• A collaborative mindset, eagerness to learn, and openness to iteration and feedback.
• Evidence of original thinking through research publications, open‑source contributions, or externally visible technical work.
• Ability to mentor others, coordinate projects, and work across disciplines.

Desirable Skills

• Exposure to quantitative trading workflows, alpha research, or electronic execution, especially in FX or other liquid asset classes.
• Experience with natural‑language‑based models, large‑scale language models, or embedding/retrieval systems.
• Familiarity with vector databases, modern data‑storage systems, and advanced data‑retrieval mechanisms.
• Experience with ML‑focused cloud tools and emerging generative‑AI platforms.

Purpose of the Role

The purpose of this role is to apply quantitative modelling, machine learning, and analytical methods to enhance trading effectiveness, client engagement, and risk understanding. This includes developing models that capture market structure, improve pricing accuracy, interpret signals, and support strategic decision‑making across the investment‑banking landscape.

Core Accountabilities

• Build and maintain quantitative models used for trading decisions, pricing frameworks, and risk analysis.
• Perform detailed research on market patterns and incorporate findings into modelling approaches.
• Deliver high‑quality analytical insights to trading and distribution teams.
• Support front‑office systems through upkeep of core analytical libraries and modelling infrastructure.
• Contribute to strategic improvements in model governance, data quality, controls, and analytical workflows.
• Drive innovation by evaluating and adapting new analytical methods or technologies.