Lead, Quantitative Forecasting Platform & GenAI Tools

JPMorgan Chase & Co.; Plano (TX); Sur place; USD 180 000 - 240 000; Plein temps

Résumé du poste

JPMorgan Chase & Co. is seeking a Quantitative Forecasting Platform Lead in Asset & Wealth Management Finance to own the roadmap for forecasting tools used in stress testing and BAU exercises.

You will lead a team of quantitative developers and data scientists, delivering production-grade Python pipelines and GenAI-enabled applications with governance and traceability. You will translate complex technical work into clear outcomes for senior stakeholders, drive sprint planning, and ensure robust

Qualifications

• 8+ years of experience in quantitative analytics, model development, data science, financial forecasting, risk modeling, or financial technology
• Strong Python engineering skills, with experience building production-grade data/model pipelines (not just notebooks)
• Proven experience implementing or productionizing quantitative forecasting models (finance, risk, stress testing, or closely related domains)
• Demonstrated ownership of an end-to-end platform or tool used by business users, including production support and lifecycle management
• Experience operating in a stakeholder-heavy environment, managing competing priorities, clarifying requirements, and driving decisions
• Strong written and verbal communication skills, with the ability to produce crisp status updates, articulate risks, and propose options
• Experience leading and developing a team of engineers or data scientists
• Understanding of controls and operational excellence across production cycles (data quality, monitoring, lineage, access, repeatability)

Responsabilités

• Own and drive the roadmap for forecasting tools supporting stress test and BAU exercises, including intake, prioritization, delivery planning, and release management
• Ensure tooling is robust, scalable, auditable, and fit-for-purpose across recurring production cycles (data ingestion, transformations, model runs, outputs/reporting, controls)
• Establish and maintain standards for code quality, documentation, testing, and operational readiness
• Implement quantitative models in Python, partnering with model owners and users to translate requirements into production-grade pipelines with full traceability
• Partner with model risk and governance stakeholders to support documentation, testing evidence, explainability, and controlled change management
• Identify, prototype, and productionize GenAI applications that improve productivity and controls for Finance forecasting and analysis workflows, with appropriate guardrails
• Serve as the single accountable owner for forecasting tool deliverables and stakeholder communications, running governance routines (status reporting, risk/issue management, decision logs)
• Translate technical details into clear outcomes, risks, and decisions for non-technical audiences
• Lead and develop a team of quantitative developers and data scientists, managing sprint/capacity planning, delivery milestones, and operational support coverage
• Coach engineers on engineering discipline (CI/CD, testing, packaging, environment management) and Finance domain context
• Ensure controls around data quality, reconciliations, run-time monitoring, lineage, access, and repeatability, and drive incident response, root-cause analysis, and compliant Model Development Life Cycle (MDLC) practices

Connaissances

Python engineering

Production-grade pipelines

Stakeholder communication

Team leadership

GenAI applications

Forecasting/modeling

Outils

CI/CD

Description du poste

JPMorgan Chase & Co. is seeking a Quantitative Forecasting Platform Lead in Asset & Wealth Management Finance to own the roadmap for forecasting tools used in stress testing and BAU exercises.

You will lead a team of quantitative developers and data scientists, delivering production-grade Python pipelines and GenAI-enabled applications with governance and traceability. You will translate complex technical work into clear outcomes for senior stakeholders, drive sprint planning, and ensure robust