Quantitative Forecasting Platform Lead – Asset Wealth Management Finance

Join Asset & Wealth Management (AWM) Finance to lead the delivery and evolution of quantitative forecasting tools that power stress testing and business-as-usual exercises. You’ll shape end-to-end Python-based tooling and GenAI-enabled applications, lead a talented team of developers and data scientists, and serve as the trusted point of contact for senior stakeholders across Finance, Risk, and Technology.
As the Quantitative Forecasting Platform Lead in Asset & Wealth Management (AWM) Finance, you will own the roadmap and delivery of forecasting tools spanning model implementation, production-grade Python tooling, and GenAI applications. You’ll lead a team of model developers and data scientists, ensure robust controls and governance, and act as the single accountable owner for deliverables and stakeholder communications. You will translate complex technical work into clear outcomes, risks, and decisions for senior, non-technical audiences.
Job Responsibilities
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
Required Qualifications, Capabilities, and Skills
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)
Preferred Qualifications, Capabilities, and Skills
Experience supporting stress testing and/or regulatory-driven forecasting processes
Familiarity with model governance / model risk management expectations (documentation, validation support, controls evidence)
Experience building GenAI applications (e.g., retrieval-augmented generation, workflow assistants, evaluation/monitoring)
Experience with modern software engineering practices (e.g., CI/CD, automated testing, dependency management)