End-to-End Technical Ownership: Take accountability for the ML lifecycle, from initial data cleaning and exploration to developing, deploying, and maintaining production-grade models and pipelines.
Experimental Rigor: Design, execute, and iterate on experiments with clear success metrics, ensuring solutions create measurable business impact.
Stakeholder Partnership & Communication: Act as a bridge between technical and non-technical teams by framing complex problems clearly, aligning solutions with business constraints, and delivering actionable findings.
Operational Excellence: Improve existing systems through iterative feature engineering and robust monitoring, while working alongside engineers to ensure all solutions are scalable and maintainable.
Strategic Opportunity Identification: Proactively identify and scope new areas for optimisation, personalisation, or automation that align with broader business goals.
Team & Industry Leadership: Uphold and evolve team standards for code quality and experimentation, providing peer mentorship and integrating modern ML developments where they provide value.

Experience applying data science or machine learning to real-world problems, through industry work, research, or applied projects
Strong Python skills, with practical use of data science and ML libraries (e.g. pandas, NumPy, scikit-learn or equivalent)
Solid SQL skills and experience working with large, structured datasets
Good understanding of core machine learning concepts, including model training, evaluation, and feature engineering
Exposure to generative AI or LLM-based systems (e.g. prompt design, evaluation of outputs, and integrating third-party model APIs into data products or pipelines)
Familiarity with software engineering practices such as version control (Git) and writing reproducible, maintainable code
Ability to translate ambiguous problems into data-driven solutions and measurable outputs
Strong collaborative mindset, with a proactive approach to problem solving
Clear communication skills, with the ability to explain technical approaches and trade-offs to non-technical stakeholders

Join the Data Science team at The Telegraph and work on applied machine learning problems at scale. You will design, build and deploy models that power key parts of our digital products. This includes working across the full lifecycle, from data exploration and model building through to productionisation and monitoring.

1 The Telegraph jobs in England & Wales