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- Tasks: Join us as an Econometrician/Data Scientist, leading data modelling and analysis.
- Company: Be part of a fast-growing marketing effectiveness agency that values its people.
- Benefits: Enjoy hybrid working, gym membership, pension, and yearly bonuses.
- Why this job: Make a real impact in a supportive culture where your ideas matter.
- Qualifications: 2-3 years of experience in Marketing Mix Modelling; strong analytical skills required.
- Other info: Opportunity for career growth in a diverse and loyal client environment.
The predicted salary is between 34000 - 45000 £ per year.
London (Hybrid working 3 office days per week)
Salary DOE £40,000-£45,000
Additional Benefits: Gym Membership, Pension and yearly bonus
We're excited to be hiring for a unique opportunity to join a fast-growing, independent marketing effectiveness agency that genuinely puts its people first. This is a chance for someone who wants to be a bigger fish in a smaller sea to step into a role where you can truly make your mark, have real influence, and accelerate your career growth as we continue to scale. With a loyal and diverse client base, and a culture built on support and empowerment, you'll be part of a team where your ideas are heard and your impact is recognised.
We're looking for a motivated and capable Econometrician / Data Scientist with 2-3 years of hands-on experience in Marketing Mix Modelling (MMM). Experience within the FMCG sector would be a bonus, but it's not essential.
Roles and Responsibilities
- This role is well-suited for candidates who have a strong analytical mindset and prefer working behind the scenes with data
- Leading the modelling process from briefing, data exploration, and variable selection through to model building, interpretation, and being involved in the presentation of results (interim and final debriefs will be presented by the Account Director)
- Creating clear and insightful output decks for both internal stakeholders and client presentations
Experience & Skills Required
- Strong econometric modelling skills using tools such as R, Python, or other statistical software packages (e.g., EViews, SAS)
- Experience with model validation, diagnostics, and performance metrics
- Ability to handle large datasets, clean and transform raw data, and apply advanced statistical techniques such as regression, lag structures, adstock, saturation, and interaction effects
- The successful candidate will be expected to take full ownership of modelling projects, from raw data ingestion through to final model delivery and client-ready outputs, with minimal supervision.
If this sounds like you then please apply! Alternatively, you can refer a friend or colleague by taking part in our fantastic referral schemes!
Econometrician / Data Scientist employer: Datatech
Contact Detail:
Datatech Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Econometrician / Data Scientist
✨Tip Number 1
Familiarise yourself with the latest trends in Marketing Mix Modelling (MMM). Being able to discuss recent developments or case studies during your interview can demonstrate your passion and knowledge in the field.
✨Tip Number 2
Network with professionals in the econometrics and data science community. Attend relevant meetups or webinars to connect with others in the industry, which could lead to valuable insights and potential referrals.
✨Tip Number 3
Prepare to showcase your technical skills in R or Python. Consider working on a personal project or contributing to open-source projects that highlight your ability to handle large datasets and apply advanced statistical techniques.
✨Tip Number 4
Research the company culture and values of the marketing effectiveness agency. Be ready to articulate how your personal values align with theirs, as this can set you apart as a candidate who fits well within their 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 experience in econometric modelling and data science. Emphasise any hands-on experience with tools like R or Python, and mention specific projects that showcase your analytical skills.
Craft a Compelling Cover Letter: Write a cover letter that reflects your passion for data science and marketing effectiveness. Discuss how your background aligns with the role and express your enthusiasm for contributing to a fast-growing agency.
Showcase Relevant Skills: In your application, clearly outline your skills in handling large datasets and applying advanced statistical techniques. Mention any experience with model validation and performance metrics, as these are crucial for the role.
Highlight Team Collaboration: Since the role involves working closely with internal stakeholders and clients, include examples of past teamwork experiences. Demonstrating your ability to communicate insights effectively will strengthen your application.
How to prepare for a job interview at Datatech
✨Showcase Your Analytical Skills
As an Econometrician/Data Scientist, your analytical mindset is crucial. Be prepared to discuss specific examples of how you've used data to drive decisions or solve problems in previous roles. Highlight any experience with Marketing Mix Modelling and how it has impacted your past projects.
✨Familiarise Yourself with Relevant Tools
Make sure you are well-versed in the tools mentioned in the job description, such as R, Python, or EViews. During the interview, be ready to talk about your proficiency with these tools and provide examples of how you've applied them in real-world scenarios.
✨Prepare for Technical Questions
Expect technical questions related to econometric modelling, including model validation and performance metrics. Brush up on key concepts like regression analysis, lag structures, and adstock effects, so you can confidently answer any queries that come your way.
✨Demonstrate Ownership and Initiative
The role requires taking full ownership of modelling projects. Share instances where you've led projects from start to finish, detailing how you managed data ingestion, model building, and delivery of client-ready outputs. This will show your potential employer that you can work independently and effectively.
