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.