2 months ago
London, United KingdomSenior
Responsibilities
- Collaborate in cross-functional teams with data scientists, software engineers, data engineers, machine learning engineers, and product managers.
- Design and deliver scalable machine learning models and AI solutions that create measurable business impact.
- Own the end-to-end ML delivery lifecycle, including data exploration, feature engineering, model selection and tuning, evaluation, deployment, and maintenance.
- Partner with stakeholders to propose data products using Trainline’s datasets and advanced algorithms.
- Create tools, frameworks, and libraries to accelerate ML and AI product delivery and improve workflows.
- Participate in the AI and ML community and promote rigorous learning and experimentation.
Requirements
- Advanced degree in Computer Science, Mathematics, Statistics, or a similar quantitative discipline.
- Proficiency in Python and open-source data libraries such as Pandas, NumPy, and scikit-learn.
- Experience productionising machine learning models and/or AI solutions.
- Expertise in predictive modelling, classification, regression, optimisation, NLP algorithms, or recommendation systems.
- Experience with Spark and familiarity with statistical methodologies, data extraction, data manipulation, and feature engineering.
- Knowledge of DevOps technologies such as Docker and Terraform, plus MLOps practices and platforms such as MLflow.
- Experience with agile delivery methodologies and CI/CD processes and tools.
- Strong communication skills.
- Preferred experience includes transport, geographical information systems, cloud infrastructure, large language models including fine-tuning, RAG and agents, and graph technology or algorithms.
Benefits
- Private healthcare and dental insurance.
- Generous 28-day work-from-abroad policy.
- Hybrid work model requiring at least 60% office attendance over a 12-week period.
- 2-for-1 share purchase plans and an electric vehicle scheme.
- Extra festive time off and family-friendly benefits.
- Clear career paths, transparent pay bands, personal learning budgets, and regular learning days.
Tech Stack
Categories
