Data Scientist

Senior Data Scientist (Optimisation \& Operations Research) **Role Title:** Senior Data Scientist – Optimisation **Location:** Waterside, UK (hybrid) **Contract Type:** Contract (Inside IR35\) **Travel:** Occasional travel to Europe required **Eligibility:** UK or EU Citizens only (mandatory) Role Overview We are seeking a **senior\-level Data Scientist with deep optimisation and operations research experience** , ideally within airline, aviation, or complex logistics environments. This role sits within a **product\-led, cross\-functional squad** responsible for building **industrialised decision\-support software** used in operationally critical environments. The successful candidate will design, develop, and productionise optimisation and machine learning models that directly influence real\-world operational decisions. This is **not a generic ML role** — strong mathematical optimisation, structured problem\-solving, and stakeholder engagement are core to success. Key Responsibilities Optimisation \& Modelling * Design and implement **advanced optimisation and decision\-support models** (e.g. LP, MIP, heuristics, metaheuristics). * Translate complex operational problems into **mathematical formulations** with clear objectives and constraints. * Prototype, test, and refine optimisation and ML models in **Python** . * Harden models for **operational use** , including edge cases and data anomalies. Full\-Stack Data Science Delivery * Build and maintain **robust data pipelines** using Python and SQL. * Industrialise models following **software engineering best practices** : * modular design * strict typing * unit and regression testing * Integrate algorithms into **workflow orchestration frameworks** (e.g. Dagster or Airflow). * Collaborate with engineers to ensure models integrate seamlessly into the wider product stack (data ingestion, UI, orchestration). Product \& Business Engagement * Work closely with business stakeholders to understand decision\-making processes, constraints, and trade\-offs. * Clearly explain optimisation approaches, assumptions, and results to **non\-technical audiences** . * Quantify and communicate **business value and impact** (e.g. cost savings, efficiency gains). * Contribute to feature prioritisation, balancing **speed of delivery vs long\-term value** . Ways of Working * Operate effectively within an **Agile product squad** . * Use Git best practices for version control, peer reviews, and documentation. * Take ownership of delivery with minimal supervision. * Mentor junior data scientists where required. Required Skills \& Experience (Must\-Have) * Proven experience in **optimisation / operations research** (not just predictive ML). * Strong Python skills with OR and DS libraries (e.g. pandas, numpy, scikit\-learn, gurobi, ortools). * Ability to structure ambiguous operational problems and reason through trade\-offs. * Experience delivering **production\-grade data science software** . * Strong communication skills with both technical and non\-technical stakeholders. * Experience working in **large\-scale, complex, data\-intensive environments** . * Eligible for travel within Europe (citizenship required). Desirable Experience (Nice\-to\-Have) * Airline, aviation, transportation, or logistics domain experience. * Exposure to safety\-critical or regulated operational environments. * Cloud experience (AWS preferred), CI/CD pipelines, Docker, workflow orchestration. * Consulting or advisory background. * Experience mentoring or leading other data scientists. Qualifications * Master’s degree (or higher) in Data Science, Operations Research, Applied Mathematics, or related field * **OR** * Equivalent relevant industry experience with a strong optimisation focus What Success Looks Like * Optimisation models are **robust, explainable, and operationally trusted** . * Clear linkage between technical solutions and **business outcomes** . * High\-quality, maintainable code deployed through standard engineering pipelines. * Strong collaboration across product, engineering, and business teams.