Come join the Firmwide Technology Resiliency group that is part of the JPMorgan Chase Cybersecurity and Technology Controls organization.

The group is tasked with ensuring the firms technology estate can maintain effective operations and support the ongoing, critical functioning of Essential Business Services in the face of todays evolving threat landscape.As a Senior Director of Software Engineering at JPMorgan Chase within the Cybersecurity and Tech Controls team, you will leverage your expertise in designing and developing sophisticated modelling software to enhance cyber and business resiliency efforts. Collaborating closely with a quantitative data scientist, you will spearhead the creation of a Bayesian inference-based modelling platform aimed at forecasting the risk and business consequences of potential disruptive events. This pivotal role plays a crucial part in guiding strategic decisions related to cyber defence, business continuity planning, regulatory compliance, and operational resilience.Leads multiple technology and process implementations across departments to achieve firmwide technology objectivesProvides leadership and high-level direction to teams while frequently overseeing employee populations across multiple platforms, divisions, and lines of businessDesign and develop scalable, production-grade software for risk modelling, inference engines, and simulation frameworksCollaborate with cybersecurity teams, risk analysts, data scientists and resiliency stakeholders to define model inputs, risk scenarios, and system architecture requirementsBayesian networks, probabilistic graphical models) into performant software modules.Develop data ingestion and transformation pipelines to source data from internal systems and threat intelligence sourcesLead the architecture design for modular, explainable, and extensible risk modelling systemsEnsure robustness, auditability, and version control of all models and underlying code per company and regulatory standardsBuild APIs and tools that enable integration with business intelligence dashboards, threat platforms, GRC systems and reporting pipelinesPartner with enterprise risk and enterprise control management teams to ensure the model outputs are interpretable and actionable for executive decision-makersFormal training or certification on software engineering concepts and expert applied experience. Experience leading complex projects supporting system design, testing, and operational stabilityStrong programming skills in Python (especially scientific libraries: and experience working with probabilistic programming frameworks (e.g. Experience designing and deploying Bayesian networks, Monte Carlo simulations, or other probabilistic models in complex real-world systems.Demonstrated experience developing enterprise-scale data modelling platforms or risk analysis toolsSolid knowledge of software architecture principles, cloud-native design (e.g. AWS/GCP), containerization (Docker, Kubernetes), and CI/CD best practicesStrong academic background with an advanced degree in either Mathematics, Data Science, Engineering, Computer Science or another quantitative field.Background in graph theory, decision theory, or risk quantification is a plusUnderstanding of cybersecurity risks, operational resilience, or business continuity frameworks in regulated industries (preferably financial services)Experience in modelling cascading failures, supply chain risk, or complex interdependency networks