Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Lead end-to-end modelling projects and deliver insightful analysis.
- Company: Join a fast-growing agency helping brands understand performance through analytics.
- Benefits: Enjoy hybrid work options and a culture of continuous improvement.
- Why this job: Be part of a collaborative team making a real impact in marketing effectiveness.
- Qualifications: 2-3 years of MMM experience and strong skills in R or Python required.
- Other info: Opportunity to work with top brands and develop your technical skills.
The predicted salary is between 36000 - 60000 £ per year.
MBN's client, an established and fast-growing marketing effectiveness agency on a mission to help brands truly understand what’s driving performance — and how to do better. Our work is grounded in rigorous analytics and trusted by some of the world’s most recognisable brands. As demand for our services grows, particularly within the FMCG space, we’re looking for a skilled Econometrician / Data Scientist with hands-on experience in Marketing Mix Modelling (MMM) to join our team.
About the Role
You’ll take ownership of end-to-end modelling projects — from data ingestion through to modelling, validation, and producing client-ready outputs. While direct client interaction is limited in this role, your work will be at the core of delivering meaningful insights and commercial value. You’ll work alongside a collaborative, ego-free team that values openness, technical rigour, and continuous improvement.
What You’ll Be Doing
- Lead MMM modelling projects: from data prep, exploration, and variable selection through to final model delivery
- Build clear, insightful analysis decks for internal teams and client presentations
- Apply econometric techniques including regression modelling, adstock, saturation, lag structures, and more
- Continuously improve processes as part of a company that invests heavily in technical development
What We’re Looking For
Must-haves:
- 2–3 years of hands-on MMM experience
- Strong modelling skills in R, Python, or similar statistical tools
- Confident handling, cleaning, and transforming large datasets
- Analytical mindset with a sharp eye for detail
- Ability to work independently on modelling tasks from start to finish
Nice-to-haves:
- Familiarity with FMCG data and media mix variables (TV, digital, OOH, promotions, etc.)
- Understanding of FMCG dynamics like seasonality, product cannibalisation, and baseline shifts
Seniority level: Mid-Senior level
Employment type: Full-time
Job function: Analyst
Industries: IT Services and IT Consulting
Contact Detail:
MBN Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Econometrician/Data Scientist
✨Tip Number 1
Familiarise yourself with Marketing Mix Modelling (MMM) techniques and be prepared to discuss your hands-on experience in this area. Highlight specific projects where you've successfully applied econometric methods, as this will demonstrate your expertise and relevance to the role.
✨Tip Number 2
Brush up on your skills in R or Python, as these are essential for the role. Consider working on a small project or two that showcases your ability to handle large datasets and perform complex analyses, which you can mention during discussions.
✨Tip Number 3
Network with professionals in the marketing effectiveness space, especially those who have experience in FMCG. Engaging in conversations about industry trends and challenges can provide valuable insights and may even lead to referrals.
✨Tip Number 4
Prepare to discuss how you approach problem-solving and continuous improvement in your work. The company values technical development, so showcasing your commitment to learning and adapting will resonate well with the hiring team.
We think you need these skills to ace Econometrician/Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your 2-3 years of hands-on experience in Marketing Mix Modelling (MMM). Emphasise your skills in R, Python, or other statistical tools, and showcase any relevant projects that demonstrate your modelling capabilities.
Craft a Compelling Cover Letter: In your cover letter, explain why you are passionate about econometrics and data science. Mention specific examples of how you've successfully handled large datasets and applied econometric techniques in previous roles, particularly in relation to FMCG.
Showcase Your Analytical Skills: During the application process, be prepared to discuss your analytical mindset. Provide examples of how you've approached data cleaning, transformation, and modelling tasks independently, and how your insights have added value to past projects.
Highlight Continuous Improvement: Mention any initiatives you've taken to improve processes in your previous roles. This could include adopting new tools, refining methodologies, or contributing to team knowledge sharing, as this aligns with the company's focus on technical development.
How to prepare for a job interview at MBN Solutions
✨Showcase Your Technical Skills
Be prepared to discuss your experience with R, Python, or other statistical tools. Bring examples of past projects where you applied econometric techniques, especially in Marketing Mix Modelling, to demonstrate your hands-on expertise.
✨Understand the FMCG Landscape
Familiarise yourself with FMCG data and media mix variables. Be ready to discuss how seasonality, product cannibalisation, and baseline shifts can impact marketing effectiveness, as this knowledge will be crucial for the role.
✨Prepare for Problem-Solving Questions
Expect questions that assess your analytical mindset and problem-solving abilities. Think of scenarios where you've had to clean and transform large datasets, and be ready to explain your thought process and the outcomes.
✨Emphasise Collaboration and Continuous Improvement
Highlight your ability to work within a team and your commitment to continuous improvement. Share examples of how you've contributed to team projects and how you’ve sought to enhance processes in your previous roles.
