What are the responsibilities and job description for the Wealth Management, Quantitative Portfolio Manager, Equities CIO position at JPMorgan Chase?
As a Quantitative Portfolio Manager (Executive Director) within Wealth Management’s Chief Investment Office (CIO) – Equities team, you will be a senior leader in a growing, innovative Equity Portfolio Management organization, reporting to the Head of Equities. You will set the quantitative research agenda, own core portfolio analytics and risk frameworks, and drive implementation of systematic, factor-based and data-driven insights for an $80bn equity portfolio benchmarked against MSCI World.
This role requires deep expertise in equity factor research, portfolio construction, and risk management—combined with the credibility to influence other senior portfolio managers and fundamental analysts. You will translate complex quantitative work into investment decisions, elevate the team’s analytical capabilities, and serve as a thought partner to CIO leadership on process, tooling, governance, and portfolio outcomes.
Responsibilities
- Quantitative leadership & investment partnership
- Act as the senior quantitative partner to the equity team, influencing security selection overlays, factor tilts, risk budgeting, and implementation choices across regional and global mandates.
- Lead the integration of quantitative signals with fundamental views, ensuring a repeatable, well-governed investment process.
- Risk model ownership & portfolio risk governance
- Own the application and interpretation of multi-factor risk models (e.g., Axioma and/or equivalent) to monitor exposures, crowding, concentration, liquidity considerations, and scenario sensitivities.
- Establish escalation frameworks and decision support for material risks; contribute to portfolio review cadence and senior risk discussions.
- Portfolio construction, optimization & attribution
- Design and improve portfolio construction frameworks including constraints, turnover control, transaction cost awareness, and rebalancing discipline.
- Lead performance attribution and factor decomposition to diagnose drivers of returns, active risk, and drawdowns; turn findings into actionable portfolio recommendations.
- Data, engineering & advanced analytics
- Drive development of scalable research and analytics tooling (Python-first), including data pipelines, reusable libraries, and standardized reporting for PM workflows.
- Evaluate and apply machine learning/AI techniques where appropriate (feature engineering, ensemble methods, NLP for alternative data), with emphasis on interpretability and investment relevance.
- Stakeholder management & communication
- Communicate complex quantitative concepts clearly to senior investment professionals; deliver crisp trade-offs and recommendations rather than “model outputs.”
- Partner with technology, data, risk, compliance, and control stakeholders to ensure model governance and appropriate use.
- Controls & compliance
Maintain a consistent focus on risk management, model governance, suitability, and adherence to applicable policies and controls.
Required Responsibilities, Capabilities and Skills:
- 12 years of experience in quantitative investing, equity research, portfolio construction, or risk analytics (buy-side preferred), with demonstrated impact on portfolio outcomes (alpha, risk-adjusted returns, drawdown control, implementation efficiency).
- Deep understanding of equity markets, factor investing, risk modeling, and portfolio construction under real-world constraints (turnover, costs, liquidity, client guidelines).
- Proven experience owning or heavily influencing risk model usage (Axioma or similar), exposure management, scenario analysis, and attribution.
- Advanced programming capability in Python, including strong applied experience with data analysis libraries (Pandas, NumPy, SciPy, stats/ML stack) and production-quality research practices (version control, testing, code review).
- Solid grounding in statistics/econometrics and familiarity with ML techniques appropriate for investment contexts (regularization, tree-based methods, cross-validation, time-series pitfalls).
Bachelor’s degree required;
Preferred Responsibilities, Capabilities and Skills:
- Master’s/PhD in a quantitative discipline (Math, CS, Engineering, Statistics, Financial Engineering, etc.) strongly preferred.
- CFA progress or designation is a plus (not required), particularly where it strengthens investment judgement and communication with fundamental stakeholders.