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.
