Senior Manager - AI Researcher, Quantitative Strategies

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.