Role Overview

This is a research role responsible for advancing AI/ML-driven investment research to support alpha generation and risk insight across the firm's quantitative and systematic investment platforms. This position is research-led and emphasizes:

Novel signal discovery and validation using modern machine learning.
Strong experimental design, robustness and model risk discipline.
Translating research into investment-ready concepts, working with quant developers for productionisation.

The AI Researcher will collaborate closely with portfolio managers, quant researchers, and risk partners to develop research that withstands out-of-sample testing, regime shifts, and implementation frictions (transaction costs, liquidity, capacity).

Key Responsibilities

1) Research Leadership in AI/ML for Investments

Lead research initiatives applying AI/ML to alpha generation and risk insights across equities, fixed income, and/or multi-asset (depending on team mandate).
Formulate hypotheses, design experiments, and drive research agendas focused on signal stability, interpretability, and economic rationale.
Evaluate and select modeling approaches (e.g., cutting edge deep learning algorithms, reinforcement learning) based on empirical evidence and implementation practicality.

2) Alpha Signal Discovery & Feature Research

Create and test predictive features from structured and alternative datasets (e.g., prices, fundamentals, macro, curves/spreads, flows, options-implied measures, text/news).
Research model families relevant to finance, including: Time-series forecasting and representation learning, cross-sectional prediction & ranking objectives, and nonlinear factor discovery and interactions.
Develop frameworks for detecting and managing non-stationarity (regime shifts, concept drift, structural breaks).

3) Research Methodology, Robustness & Model Risk Discipline

Establish and enforce rigorous research standards, including: leakage controls, realistic signal timing, corporate action adjustments walk-forward evaluation, time-series cross-validation, and stability diagnostics sensitivity testing across sub-periods, regimes, and market stress events.
Diagnose and mitigate overfitting through sound regularization, feature selection discipline, and robust validation.
Produce research documentation suitable for internal governance, including assumptions, limitations, failure modes, and monitoring metrics.
Contribute to model risk processes: validation support, explainability, and audit-ready research artifacts.

4) Portfolio & Implementation-Aware Research

Translate model outputs into implementable signal definitions (ranking, scoring, forecasts) aligned to portfolio construction approaches.
Incorporate practical constraints early: turnover, liquidity, transaction costs, latency/data availability, and capacity.
Partner with portfolio construction and execution teams to ensure research remains robust after cost and implementation adjustments.

5) Research Communication & Stakeholder Influence

Present research findings to investment leadership with clarity: economic intuition, empirical results, and risk considerations.
Contribute to the firm's though leadership by authoring and publishing sanitized AI research and methodological advancements in leading conferences and quantitative finance journals.
Mentor and guide junior researchers on methodology, experimental design, and research hygiene.

6) Research-to-Production Collaboration

Work with quant developers/engineering teams to transition validated research into production pipelines.
Define requirements, acceptance criteria, and monitoring KPIs support post-launch research review and model drift investigations.
Maintain an iterative research lifecycle: improvements, recalibration, and controlled retirement of decaying signals.

Required Qualifications, Skills & Capabilities

Core AI/ML Research Skills (Required):

Strong foundation in statistical learning theory.
Expertise in time-series modelling and optimization.
Practical experience with explainability and diagnostics (e.g., SHAP, permutation importance, stability tests) appropriate for investment oversight.

Programming & Research Tooling:

Advanced R or Python or Julia for research.
Experience with deep learning frameworks (e.g. PyTorch / Flux.jl / TensorFlow).
Strong research hygiene: Git, reproducible experiments, notebooks-to-library workflows, and structured documentation.
Familiarity with experiment tracking tools (MLflow/W&B or equivalent) is beneficial.

Data Competency:

Strong skills in dataset curation, construction and labelling, including handling: survivorship bias, look-ahead bias, delayed data availability and corporate actions, missingness, outliers, vendor idiosyncrasies.
Proficiency with SQL and working with large datasets comfort partnering with data engineering teams.

Markets & Portfolio Context:

Working understanding of market microstructure and implementation constraints (transaction costs, liquidity, slippage).
Portfolio concepts: risk factors, diversification, drawdown, turnover, and capacity.
Domain knowledge in at least one area (equities or fixed income) preferred.

Experience & Knowledge Required

Education:

Master's or PhD strongly preferred in Machine Learning, Statistics, Computer Science, Applied Mathematics, Physics, Engineering, or related fields.

Professional Experience:

Typically, 6-8+ years in ML/AI academic research, postdoc, quant research, or systematic investing (buy-side preferred strong sell-side or fintech acceptable).

Demonstrated track record of original research that improved outcomes.
Experience influencing research direction, mentoring others, and partnering with cross-functional stakeholders.

Evidence of Research Depth:

Peer-reviewed publications, patents, open-source contributions, or significant internal research outputs.
Evidence of rigorous validation and an ability to explain why models work (or fail) across regimes.

Key Competencies:

Research leadership: sets direction, prioritizes high-impact questions, and drives rigor.
Intellectual honesty and skepticism resists overfitting and backtest-first thinking.
Clear communication: simplifies complexity without overselling results.
Collaboration: effective partner to PMs, risk, and engineering pragmatic about implementation realities.

Senior Manager / Assistant Director - AI Researcher, Quantitative Strategies