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Data Scientist – Supply Chain Optimisation

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Job Description

Data Scientist – Optimisation & Operations Research

North West London (Hybrid, 3 days on-site) | £550 – £750 /day

The Opportunity

We're recruiting on behalf of a globally recognised organisation undergoing a major transformation in how it uses data to drive operational decisions. This is a rare chance to work on genuinely complex, high-impact decision-support software – embedding cutting-edge optimisation and machine learning directly into live operations.

You'll join a high-performing, Agile product squad as a full-stack Data Scientist, sitting at the intersection of data engineering, ML, and operations research.

What You'll Be Doing

Designing and delivering optimisation and ML models (linear/mixed-integer programming, heuristics, supervised/unsupervised learning) in Python, from prototype to production
Building robust, automated data pipelines and integrating models into cloud-based deployment pipelines with CI/CD
Owning features end-to-end – from stakeholder requirements through to algorithm hardening, edge-case handling, and value measurement
Working with orchestration frameworks (Dagster/Airflow), experiment tracking (MLflow), and containerised infrastructure (Docker/ECS)
Collaborating closely with business stakeholders and contributing to roadmap and feature prioritisation What We're Looking For

Strong operational research and optimisation background – this is a must
Fluent Python, with hands-on experience of scikit-learn, pandas, numpy, Gurobi or similar OR packages
Production ML/optimisation software experience – you've shipped models that run at scale
Cloud platform experience (AWS preferred); familiarity with SageMaker, DVC, GitHub Actions a bonus
Strong SQL and data engineering fundamentals
Experience in airline, aviation, transport, or engineering/maintenance environments is highly desirable
Master's degree (or equivalent) in Data Science, ML, or Operational Research – or strong demonstrable industry experience

Job Summary

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Hounslow Location
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