Senior Data Scientist – B2B

Overview

As a Senior Data Scientist at Trainline, you will influence product strategy and business outcomes using advanced analytics. You will be embedded with Trainline Partner Solutions to drive data-driven decisions for B2B rail travel solutions across Europe. Your work will focus on measurable impact through experimentation, metrics, and advanced modeling in a fast-moving, cross-functional environment. You’ll shape the direction of the product team with insights drawn from a complex data ecosystem, contributing to scalable, innovative rail travel products.

Pay / Benefits

• private healthcare & dental
• work from abroad policy
• 2-for-1 share purchase plans
• EV Scheme to reduce carbon emissions
• extra festive time off
• family-friendly benefits

Responsibilities

• Develop deep understanding of product experiences and growth opportunities.
• Contribute to roadmap and goals for the product team.
• Lead cross-functional reviews to track progress toward goals.
• Identify and articulate new product opportunities to steer team direction.
• Support experiments and launches; enable growth via data-driven decisions.
• Define focus through metrics to enable learning from experiments and releases.

Key requirements

• 5+ years of commercial data science/analytics experience driving business decisions
• Ability to distill complex analyses into clear insights for all levels, including senior management
• Experience with product engagement evaluation and measurement of success (e.g., A/B testing, front-end data analysis)
• Ability to navigate complex data sets to derive actionable insights
• Strong PowerPoint and presentation/communication skills
• Strong data visualization skills (Tableau, Spotfire, Power BI)
• Expertise in predictive modelling (parametric and non-parametric) and ML techniques
• Tech Stack: SQL, Python, R, Tableau, AWS Athena

• clear and effective communication to diverse stakeholders
• problem-solving oriented
• collaboration across cross-functional teams
• predictive modelling (logit/probit, random forest, neural nets)
• ML techniques including clustering
• data visualization (Tableau, Power BI, Spotfire)

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