Data Scientist - ML Ops

Job title: Data Scientist

Salary: £72-85kGrade: Band 3

Contract type: TfL                                                    Reference: 5544

Contract details: FTC – 12 months                  Location: Pier Walk, North Greenwich

Application closing date: 8/9/2026 at 23:59

All offers of employment are subject to satisfactory right‑to‑work checks. Candidates must be able to demonstrate their right to work in the UK.  At the present time TfL is unable to offer visa sponsorship for this role.

*Hybrid working within this role enables a balance of 50 per cent of time split between the office and home over a 4-week period. Hybrid working arrangements can evolve subject to business requirements.

Overview of project/role

The Data Scientist will collaborate closely with the Lead Data Scientist and Principal Data Scientist and cross-functional teams to design, deliver and continually enhance advanced data-driven solutions and analytical insights for TfL. You'll prepare, structure and analyse diverse datasets (structured and unstructured) to ensure they're robust, reliable and fit for analytical purposes, including suitability for leveraging AI and Generative AI techniques. 

Your role requires you to apply statistical, mathematical and scientific methods including exploratory data analysis, predictive modelling, machine learning, deep learning, hypothesis testing, optimisation techniques and emerging Generative AI methodologies to extract meaningful insights that inform strategic and operational decisions. You'll confidently evaluate analytical models and methodologies, refining and validating their effectiveness, with particular attention to optimising the performance of machine learning models. 

You'll engage proactively with stakeholders to define business problems clearly and translate them into analytical projects. Your strong communication skills will ensure that complex findings are presented clearly through compelling visualisations and narratives, tailored to technical and non-technical audiences alike, highlighting potential applications of AI-driven solutions. 

As part of your responsibilities, you'll explore innovative analytical techniques, contributing to TfL’s culture of continuous learning and improvement, including staying abreast of advancements in AI and Generative AI. You'll also promote adherence to best practices (including ethical standards) for model training and development, deployment and performance monitoring. 

Key Responsibilities:

• Build, test and iteratively refine scripts and algorithms using data science programming best practices, including version control and reproducibility and developing in, and helping to shape, an ML Ops framework and environment
• Develop robust analytical solutions and algorithms from extensive customer and operational datasets, including ticketing, sensor, telemetry and vehicle log data. 
• Integrate and analyse complex datasets to derive actionable insights supporting key operational and strategic decisions across TfL. 
• Ensure the practical application of analytical findings, providing development teams with clear methodologies ready for implementation, including applications leveraging AI and machine learning. 
• Identify operational efficiencies and opportunities for improvement through rigorous analytical approaches. 
• Research, prototype, test and enhance innovative approaches in machine learning, deep learning, AI and Generative AI. 
• Actively promote best practices and standardisation within TfL’s data science community. 
• Work collaboratively with data engineers and architects to ensure efficient, reliable data pipelines and identify opportunities to enhance data quality and accessibility. 
• Develop a clear understanding of product delivery methods, including agile methodologies and machine learning Ops, and apply these principles to manage priorities effectively. 
• Collaborate closely with product managers and other specialists to define requirements for a data science solution ensuring alignment with product goals and business objectives. 
• Actively participate in the development and mentorship of data science graduates and associate data scientists, fostering a culture of learning and capability growth within the data science team.&nbs