The Product Analytics Lead owns the end-to-end quantitative agenda for JPMorgan Personal Investing - Brokerage, integrating advanced statistical methods, machine learning, and portfolio theory into production-grade solutions and insights. The role designs and deploys propensity models to deepen client engagement, builds portfolio construction frameworks grounded in Monte Carlo and optimization techniques, and delivers rigorous business analytics for senior stakeholders.
Job responsibilities:
- Develop and enhance quantitative tools and reusable frameworks that support investment decision-making, product development, and ongoing performance monitoring.
- Own end-to-end business analytics for the Brokerage team, transforming complex datasets into strategic insights and clear product recommendations for senior stakeholders.
- Conduct applied research on statistical, machine learning, and mathematical methods relevant to brokerage and personal investing use cases.
- Build and maintain portfolio construction methodologies using Monte Carlo simulations, optimization algorithms, and risk models that align with client objectives and guardrails.
- Lead the design, development, and deployment of propensity models and scoring frameworks that increase client acquisition, activation, cross-sell, and retention.
- Partner with technology, product, and business teams to integrate models and analytics into production systems with appropriate scalability, reliability, and controls.
- Communicate findings, trade-offs, and recommendations with clarity and business context to leadership and cross-functional partners.
Required qualifications, capabilities and skills
- Advanced degree (Master’s or PhD) in Mathematics, Statistics, Physics, Financial Engineering, Computer Science, or a related quantitative discipline.
- Significant experience in quantitative analytics, financial modelling, or a related role within financial services, with a demonstrated record of productionizing models.
- Strong proficiency in Python and SQL, including experience with scientific computing and machine learning libraries (NumPy, SciPy, pandas, scikit-learn).
- Deep expertise in statistical modelling, probability theory, Monte Carlo methods, and optimization techniques.
- Experience building propensity models, scoring systems, or recommendation engines tied to measurable business outcomes.
- Ability to convey complex quantitative concepts to non-technical stakeholders with precision and brevity.
- Strong understanding of investment products, portfolio theory, and brokerage operations.
- Passion for investments with an entrepreneurial and business oriented mindset.
Preferred qualifications, capabilities and skills
- Familiarity with cloud platforms (AWS, GCP, or Azure) for scalable data processing and model deployment.
- Knowledge of regulatory considerations relevant to brokerage and personal investing.
- Experience within model risk management or model governance frameworks.
- Prior experience in brokerage, wealth management, or asset management environments.
- Fluent in other European languages (beyond English)