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Requirements
- Bachelor's or Master's in Operations Research, Econometrics, Industrial Engineering, Applied Mathematics, Data Science, or a related quantitative field. PhD preferred
- 6+ years practical experience with optimisation (LP/MILP, heuristics) and simulation (DES/ABM), plus working knowledge of causal inference or experimentation at scale
- Demonstrated ownership of end‑to‑end decision solutions in production or pilots
- Proficient in Python and SQL with strong engineering practices; comfortable with containers and CI/CD; experience integrating models into services, workflows, or apps
- Able to choose and justify methods, diagnose performance, and communicate trade‑offs to technical and non‑technical stakeholders
- Experience mentoring others and establishing modelling standards and review practices
- Problem framing: Lead discovery to define decision objectives, constraints, KPIs, and data readiness; quantify expected value and risks
- Architecture and methods: Select modelling approaches, design modular architectures, and combine optimisation, simulation, and causal evidence when appropriate
- Build and scale: Implement core models, performance‑tune solvers/simulators, and integrate with data pipelines, APIs, and front‑end experiences
- Experimentation and evaluation: Design robust experiments or quasi‑experiments; set evaluation protocols, stress tests, and rollback plans
- Reuse and standards: Create reusable components, templates, and documentation; uplift code quality, testing, and observability
- Stakeholder leadership: Run technical workshops, translate insights into decisions, and drive adoption with clear narratives and visuals
- Mentorship: Review associate work, coach on method selection and design, and help set sprint scope and acceptance criteria
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