Key Notes
Marketing measurement is the specialist focus, using Python, causal inference and geo-experimentation to assess incremental campaign impact and inform investment decisions. The portfolio combines multiple data sources where no single method is definitive and requires clear communication of assumptions and confidence. Trainline operates a hybrid model with at least 60% office attendance over twelve weeks; relevant expertise includes practical Python and experimental-method experience.
What You'll Work On
- Design marketing-measurement approaches that estimate the effectiveness and incremental impact of Trainline campaigns and customer-growth activity.
- Apply Python, causal inference and geo-experimentation to analyse performance across multiple datasets and imperfect measurement conditions.
- Translate analytical findings, assumptions and confidence levels into recommendations for marketing stakeholders making decisions across investment reviews.
- Manage several measurement projects while defining questions and scope with Marketing, Analytics and other cross-functional partners.
- Develop Trainline’s marketing-measurement methods and tools by identifying practical improvements across recurring campaign-analysis workflows used by Customer Growth teams.
Why This Role Matters
- Trainline’s marketing investment decisions are tested against incremental-impact evidence, helping stakeholders distinguish campaign effects from changes that would have occurred without the activity.
- Customer Growth measurement combines causal inference, geo-experimentation and multiple data signals, making uncertainty and limitations visible when no single method provides a definitive answer.
What They Are Looking For
- Technical: Strong practical Python experience for data analysis and measurement design is sought.
- Experience: Causal inference, geo-experimentation or related experimental/quasi-experimental work on real business questions is sought.
- Preferred: Marketing measurement, effectiveness or marketing-mix-modelling experience is ideal; relevant transferable experience is also welcomed.