Data Science Manager (B2B)

Trainline
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Science Manager

Data Science Manager

Data Science Manager

Data Science Manager

Data Science Manager

Data Science Manager - Telematics

Job Description

Most of our Talent team are currently on leave for the holiday period, so your application is likely to be reviewed in January. Enjoy the break, we’ll get back to you in the new year! 

Data Science Manager London (Hybrid, 40% in office)  £Salary + Bonus + Benefits  

Introducing Data Science & Analytics at Trainline   

Data Science & Analytics (DSA) is central to how we build products, delight our customers and grow our business. Our team members are embedded in cross-functional teams which exist across product and marketing.  Data Scientists & Analysts have a high degree of autonomy and are empowered to drive the success of their teams, using all data and techniques at their disposal.     

This is a perfect role for a Senior Data Scientist looking to move into management or a hands on Data Science Manager, you will start in the role in a more hands on capacity, learning the space and data, with headcount to hire 1 Data Analyst initially and this expected to grow over time. You’ll be focussed in Trainline Partner Solutions (TPS). This is our B2B arm and comprises white label products for carriers, and our Business Travel Booking Platform. As the most senior Data Science & Analytics person in TPS you’ll be responsible for working in cross-functional, senior team to lead the strategy for the space, influence data engineering/BI roadmaps to help develop the data maturity and develop the test and learn, goaling drive approach to product development.   

Our Data Science leaders need to wear two hats, as an expert in leader of the data science and analytics space, but also as a business lead in the company. As a leader in TPS, you will work with your Product, Marketing, Engineering and Commercial counterparts to set out the long-term strategy for the teams at Trainline and ensure we execute on this within the team whilst being responsible for communicating this vision and progress to it to senior leadership within the company.   

You will also have a close working relationship with Data Engineering, Machine Learning and BI teams as the data representative in the Pillar leads group to ensure all of data is working on the right problems to move Trainline forwards.  

Management experience is desired, but, for the right candidate, a long history of success as an individual contributor and a desire to progress your career into management is also acceptable. Experience working in a data driven tech organization is also desired, while a history of thinking strategically in a data driven way is required.  

As a Data Science Manager at Trainline, you will...  

Be responsible for influencing product, marketing and business outcomes, have the autonomy to make things happen and must obsess about having business impact. More specifically you will:  

  • Lead a team of ~1 Data Scientists / Analysts  
  • Drive a high standard of work and hold a high bar for impact within your org  
  • Mature how we achieve growth in a data driven way across your team  
  • Lead, with your cross functional counterparts, the strategy and delivery of the TPS 
  • Think big, clearly setting out a strategy and ensuring data driven execution  

Our tech stack   

  • SQL, Python, R, Tableau, Power BI, AWS Athena + more! 


Qualifications

We'd love to hear from you if you...   

  • Have experience in leading data driven decision making for a tech product, ideally with some relevant experience in the B2B space   
  • Have experience managing a data driven team and holding a high bar for analysis for 1+ years  
  • Have experience in driving growth in an online product for 6+ years  
  • Have experience setting the strategic direction and thinking big  
  • Hold the ability to distil and communicate results of complex analysis clearly and effectively to all levels including senior management   
  • Have experience of marketing evaluation and measurement of success. For example, running holdout/incrementality testing to evaluate campaign effectiveness or deploying new bidding models and understanding their impact
  • Hold the ability to navigate data sets of varying complexity/ambiguity and conduct analysis to derive clear insights and actionable results   
  • Possess strong PowerPoint and presentation/communication skills   
  • Possess strong data visualisation skills using tools like Tableau, Spotfire, Power BI etc.   
  • Have knowledge of statistical techniques like econometric modelling   
  • Hold a university degree in subject with statistical elements, i.e. maths / economics 



Additional Information

The interview process   

  1. Recruiter call (30 mins) 
  2. 1st stage interview with Head of or Director of Data Science (30 minutes) 
  3. 2nd stage loop interviews (x3 30 minutes) 
  4. Case study review panel interview (60 minutes) 

Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy, 2-for-1 share purchase plans, extra festive time off, and excellent family-friendly benefits.

We prioritise career growth with clear career paths, transparent pay bands, personal learning budgets, and regular learning days. Jump on board and supercharge your career from day one!

Our values represent the things that matter most to us and what we live and breathe every day, in everything we do:

  •  Think Big - We're building the future of rail
  • ✔️ Own It - We focus on every customer, partner and journey
  • Travel Together - We're one team
  • ♻️ Do Good - We make a positive impact

Interested in finding out more about what it's like to work at Trainline? Why not check us out on LinkedInInstagram and Glassdoor.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.