29 days ago
London, United KingdomSenior
Responsibilities
- Collaborate with data scientists, software engineers, data engineers, and product managers in cross-functional teams.
- Design and deliver machine learning models at scale with measurable business impact.
- Own the end-to-end ML lifecycle, including data exploration, feature engineering, model selection, tuning, evaluation, deployment, and maintenance.
- Shape technical direction and make scalable architectural and modelling decisions.
- Partner with stakeholders to propose innovative data products using Trainline’s datasets and current algorithms.
- Build tools, frameworks, and libraries that accelerate ML product delivery and improve workflows.
- Mentor less experienced engineers without formal people management responsibility.
- Participate in Trainline’s AI and ML community and promote rigorous learning and experimentation.
Requirements
- Advanced degree in Computer Science, Mathematics, or a related quantitative discipline, or equivalent experience.
- Considerable experience productionising machine learning models in areas such as predictive modelling, classification, regression, optimisation, or recommendation systems.
- Strong proficiency in Python, Pandas, NumPy, and Scikit-learn.
- Strong grounding in statistical methodologies, data extraction, data manipulation, and feature engineering.
- Experience with Spark, agile delivery methods, and CI/CD practices.
- Familiarity with DevOps and MLOps tools and practices, including Docker, Terraform, and MLFlow.
- Confidence communicating with and influencing technical and non-technical stakeholders.
- Preferred experience includes cloud infrastructure, NLP, large language models, fine-tuning, RAG, agents, graph technologies, transport, or geographic information systems.
Benefits
- Private healthcare and dental insurance, a work-from-abroad policy, 2-for-1 share purchase plans, an EV Scheme, extra festive time off, and family-friendly benefits.
- Clear career paths, transparent pay bands, personal learning budgets, and regular learning days.
- Hybrid work model requiring office attendance at least 60% of the time over a 12-week period.
- 28-day Work from Abroad policy.
Tech Stack
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