Day to day

• Lead end-to-end ML and optimisation projects, from concept through deployment and post-launch performance analysis.
• Build, test, and refine predictive and prescriptive models that deliver tangible business outcomes.
• Collaborate with data engineers and ML engineers to design robust pipelines integrated with CI/CD workflows, ensuring models are reproducible, version-controlled, and continuously deployed with confidence.
• Implement monitoring and retraining frameworks to maintain model performance and governance over time.
• Contribute to our internal ML frameworks and tooling, streamlining how we experiment, validate, and deploy models at scale.
• Partner with stakeholders across the airline to translate complex analytical results into clear, actionable recommendations.
• Stay curious; keep up with developments in ML, GenAI, and responsible-AI practices, bringing new ideas to how we innovate with data.

About you

You’re an experienced, impact-driven data scientist who has seen multiple projects through the full lifecycle, from Jupyter notebook to production API. You combine deep technical expertise with commercial understanding and thrive on collaboration.

You’ll bring:

• 5 + years’ experience delivering applied machine-learning projects in production.
• Proven record of deploying and maintaining ML models through CI/CD pipelines
• Advanced proficiency in Python (pandas, scikit-learn, PySpark) and SQL.
• Experience with ML lifecycle tooling such as MLflow and Databricks.
• Strong understanding of testing, version control, containerisation, and monitoring.
• Excellent communication skills — able to convey complex ideas clearly to technical and non-technical audiences alike.
• A degree, PHD or post-doc experience in a quantitative discipline such as statistics, mathematics, computer science, or a related field.

Nice to have:

Experience applying GenAI or NLP models to real-world business problems.