At a Glance
- Tasks: Build ML models that drive commercial decisions and analyse marketing effectiveness.
- Company: Global live entertainment business investing in Data Science for transformation.
- Benefits: Competitive salary, hybrid working model, and opportunities for impactful projects.
- Other info: Dynamic environment with significant career growth potential.
- Why this job: Join a rapidly scaling team and make a real difference in marketing analytics.
- Qualifications: Strong ML and statistical modelling skills; quick learners welcome.
The predicted salary is between 45000 - 55000 € per year.
Do you want to build ML models that directly influence commercial decisions? Have you delivered measurable business impact through experimentation or optimisation? Are you ready to work on large-scale marketing effectiveness and MMM projects? A global live entertainment business is investing heavily in its Data Science capability as part of a wider transformation across CRM, marketing effectiveness, and commercial growth. With the team rapidly scaling across the UK and US, this is an opportunity to join a highly visible function working on projects with real business impact.
This role is ideal for a hands-on Data Scientist with strong ML and statistical modelling foundations who wants exposure to experimentation, marketing analytics, and large-scale commercial decision making. MMM experience is not required — they’re looking for someone technically strong who can learn quickly and deliver measurable outcomes.
Key Responsibilities- Build and refine machine learning and statistical models
- Design and analyse A/B tests and experimentation frameworks
- Work on marketing effectiveness and MMM initiatives
- Develop end-to-end data science solutions with measurable business impact
- Collaborate with analysts and stakeholders on measurement plans
- Communicate insights through dashboards and data storytelling
- Contribute to recommendation and propensity modelling projects
- Salary: £45k–£55k + 10% bonus
- Working model: Hybrid (2–3 days in Central London)
- Tech stack: Python, SQL, AWS/Azure/GCP, ML modelling
- Visa sponsorship: Cannot sponsor
Interested? Please apply below.
Data Scientist - Global Entertainment, Marketing in London employer: Harnham
Join a dynamic global live entertainment business that prioritises innovation and growth in its Data Science capabilities. With a hybrid working model based in Central London, employees benefit from a collaborative work culture that fosters professional development and offers opportunities to make a tangible impact on marketing effectiveness and commercial strategies. The company is committed to nurturing talent, providing a platform for hands-on experience with cutting-edge technologies, and encouraging a data-driven approach to decision-making.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist - Global Entertainment, Marketing in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those already working in data science roles. Use platforms like LinkedIn to connect and engage with them; you never know who might have a lead on your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML models and any projects you've worked on. This is your chance to demonstrate your hands-on experience and how you can deliver measurable outcomes, just like they’re looking for.
✨Tip Number 3
Prepare for interviews by brushing up on your A/B testing and experimentation frameworks. Be ready to discuss how you’ve used these in past projects, as this will show you can hit the ground running in their marketing effectiveness initiatives.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Data Scientist - Global Entertainment, Marketing in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Data Scientist role. Highlight your ML and statistical modelling experience, and don’t forget to mention any relevant projects that showcase your ability to deliver measurable business impact.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you’re excited about this opportunity. Share specific examples of how you've used data science to influence commercial decisions or improve marketing effectiveness. This is your chance to show your personality and passion!
Showcase Your Technical Skills:Since we’re looking for someone technically strong, make sure to include your proficiency in Python, SQL, and any cloud platforms you’ve worked with. If you have experience with A/B testing or experimentation frameworks, definitely highlight that too!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen on joining our team!
How to prepare for a job interview at Harnham
✨Know Your ML Models
Make sure you brush up on your machine learning models before the interview. Be ready to discuss how you've built and refined models in the past, and think about specific examples where your work has led to measurable business impact.
✨A/B Testing Insights
Since the role involves designing and analysing A/B tests, prepare to talk about your experience with experimentation frameworks. Have a couple of examples ready that showcase how your testing has influenced marketing decisions or optimised processes.
✨Data Storytelling Skills
This position requires strong communication skills, especially in data storytelling. Think about how you can present complex data insights in a clear and engaging way. Practise explaining your findings as if you're talking to someone without a technical background.
✨Collaborative Mindset
Collaboration is key in this role, so be prepared to discuss how you've worked with analysts and stakeholders in the past. Highlight any experiences where you contributed to measurement plans or worked on cross-functional projects to achieve common goals.
