Lead AI/ML Applied Scientist

Optum is a global organisation that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy

, data, and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive

, and career development opportunities. Come make an impact on the communities we serve as you help us advance healthcare innovation on a global scale. We are seeking a highly experienced Lead AI/ML Engineer to lead the discovery, design, and adoption of advanced optimisation and AI/ML solutions across mathematical programming, quantum-inspired methods, hybrid ML + optimisation, and Generative AI domains. This role serves as a senior technical leader responsible for driving optimisation innovation, solving complex healthcare business problems, defining scalable solution strategies, and accelerating the transition of optimisation solutions from experimentation to production. Owns optimisation strategy, architecture decisions, enterprise standards, reusable frameworks, capability development, and leadership of small teams while remaining deeply hands-on in solving critical business challenges. Primary

Optimisation Strategy & Technical Leadership • Drive optimisation and AI solution strategy for complex, high-impact healthcare business problems. • Lead technical design, solution architecture, and optimisation technology selection decisions. • Establish reusable optimisation patterns, solver frameworks, standards, and best practices across the organisation. • Provide technical leadership and mentorship to AI/ML Engineers and cross-functional teams. • Evaluate emerging optimisation, quantum, and AI technologies and recommend enterprise adoption approaches. • Influence enterprise AI and optimisation strategy, architecture standards, and capability development. Optimisation & AI/ML Modelling • Define the modelling strategy for complex business problems, setting the approach for mathematical optimisation and AI/ML solution design across the enterprise. • Set strategic direction for advanced AI/ML and optimisation model development across predictive, prescriptive, deep learning, and GenAI systems. • Own the formulation strategy for complex optimisation problems, including linear and non-linear programming, integer and combinatorial optimisation, and stochastic and robust optimisation. • Lead the development of new modelling paradigms combining ML and optimisation, including decision-focused learning, reinforcement learning, and constrained optimisation. • Drive the enterprise strategy for GenAI and optimisation integration, including retrieval optimisation, prompt optimisation, and constrained generation frameworks. • Architect scalable modelling frameworks that integrate optimisation solvers with ML/AI systems for enterprise-wide deployment. • Champion quantum and quantum-inspired optimisation methods, including QAOA, annealing approaches, and hybrid quantum-classical algorithms. Applied Solution Development • Design and develop POCs, prototypes, and reference implementations for optimisation-driven use cases. • Build reusable assets including solver configurations, optimisation workflows, evaluation frameworks, and implementation accelerators. • Define production-ready solution blueprints to support engineering adoption of optimisation solutions. • Lead end-to-end lifecycle activities including problem formulation, modelling, solver selection, validation, deployment, monitoring, and continuous improvement. Production Readiness & MLOps • Drive successful transition of validated optimisation solutions into production by partnering with engineering teams to ensure scalability, maintainability, and security. • Apply MLOps best practices including experiment tracking, solver versioning, CI/CD integration, performance monitoring, and observability. • Ensure operational readiness, model governance, and alignment with enterprise architecture standards. • Develop implementation-ready artefacts including reusable code, optimisation pipelines, solver integration patterns, and technical documentation. Research & Innovation • Define the research agenda in optimisation, operations research, and quantum computing, directing investigation into high-impact healthcare applications. • Lead evaluation and enterprise adoption decisions for emerging AI and optimisation frameworks and technology stacks. • Lead and sponsor publication of research artefacts including white papers, patents, and internal frameworks. • Drive adoption of optimisation accelerators, reusable frameworks, and best practices across teams. Responsible AI & Compliance • Establish evaluation, guardrail, and governance frameworks for optimisation and AI solutions. • Ensure explainability of optimisation decisions, fairness constraints, and regulatory alignment with HIPAA/PHI, SOC 2, and HITRUST. • Apply responsible AI principles, bias mitigation, and AI governance frameworks throughout the solution lifecycle. Team & Organisational Impact • Lead a small team of AI/ML Engineers while remaining deeply hands-on in optimisation and AI solution development. • Mentor team members on optimisation methodologies, mathematical modelling, experimentation practices, and technical excellence. • Promote knowledge sharing, innovation, and adoption of reusable optimisation and AI capabilities. • Collaborate with business, product, architecture, and engineering teams to align solutions with measurable business outcomes. • Communicate solution results, trade-offs, and business impact to technical and non-technical stakeholders. Stakeholder Engagement & Leadership • Accelerate organisational adoption of optimisation and AI by establishing repeatable patterns, reusable frameworks, and governance standards that reduce time-to-production. • Influence enterprise AI and optimisation strategy through thought leadership, stakeholder engagement, and cross-functional collaboration. • Communicate research findings, solution performance, strategic trade-offs, and business impact clearly to executive and non-technical stakeholders. • Lead and own technical solution design discussions, providing authoritative AI and optimisation architecture recommendations that balance business objectives, solver performance, scalability, and compliance