Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Lead data workflows and build advanced Bayesian models to optimise marketing strategies.
- Company: Join a leading FMCG brand known for innovation and excellence in marketing effectiveness.
- Benefits: Enjoy hybrid working, potential long-term contract, and opportunities for professional growth.
- Why this job: Be part of a high-performing team making impactful decisions through data-driven insights.
- Qualifications: Expertise in Python, Bayesian methods, and advanced statistical modelling required; MSc or PhD preferred.
- Other info: Initial 12-month contract with the possibility of extension due to project scope.
The predicted salary is between 43200 - 72000 £ per year.
Location: London (Hybrid Working)
Contract Type: Initial 12 month contract + potential to extend long-term due to 3/4 year project scope.
Start Date: ASAP
We have an excellent opportunity for a Bayesian Data Scientist to join a leading FMCG Brand. This is an exciting opportunity to join a high-performing Data Science team focused on advancing marketing effectiveness through advanced econometric modelling, including Bayesian Marketing Mix Modelling (MMM), Multi-Touch Attribution (MTA), and data-driven optimization strategies.
Key Responsibilities
- Lead and manage data workflows: data extraction, transformation, validation, and exploratory analysis to ensure modelling-readiness.
- Build and refine Bayesian MMM models that capture the drivers of key marketing and commercial KPIs.
- Use Python (and optionally R) to design, build, and improve base and advanced models, integrating prior knowledge, probabilistic reasoning, and real-world constraints.
- Develop and present ROI workbooks, response curves, and optimization frameworks for marketing budget allocation.
- Run scenario-based simulations to support strategic planning and forward-looking marketing investment decisions.
- Validate and stress-test models, identifying opportunities for improvement and ensuring robustness, interpretability, and business relevance.
Requirements
- Extensive experience in building and deploying Marketing Mix Models, with a strong focus on Bayesian methods.
- Expert-level proficiency in Python, especially with pandas, NumPy, and probabilistic programming libraries such as PyMC.
- Experience with R is a bonus, particularly for MMM-related workflows.
- Deep understanding of regression modelling, Bayesian inference, hierarchical models, and MCMC techniques.
- Proven ability to handle and analyse large, complex datasets using SQL and/or Spark.
- Solid knowledge of applied statistics, modelling techniques, and the mathematical underpinnings of inference and simulation.
- Familiarity with cloud platforms (Azure preferred) and modern data science toolkits.
- Advanced degree (MSc or PhD) in Statistics, Data Science, Applied Mathematics, Computer Science, or a related quantitative field.
Preferred Attributes
- Strong foundation in optimization, simulation modelling, and decision analytics.
- Demonstrated ability to translate complex Bayesian models into strategic insights and practical business outcomes.
- Strong communication skills and the ability to collaborate across marketing, analytics, and commercial teams.
Bayesian Data Scientist employer: ECM Talent
Contact Detail:
ECM Talent Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Bayesian Data Scientist
✨Tip Number 1
Familiarise yourself with the latest trends in Bayesian methods and Marketing Mix Modelling. This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews, showcasing your passion and knowledge.
✨Tip Number 2
Network with professionals in the data science and marketing fields, especially those who have experience with Bayesian techniques. Attend relevant meetups or webinars to build connections and gain insights that could give you an edge in your application.
✨Tip Number 3
Prepare to discuss specific projects where you've applied Bayesian methods or built Marketing Mix Models. Be ready to explain your thought process, the challenges you faced, and how you overcame them, as this demonstrates your practical experience.
✨Tip Number 4
Showcase your proficiency in Python and any experience with R by working on personal projects or contributing to open-source initiatives. This hands-on experience can be a great talking point in interviews and can set you apart from other candidates.
We think you need these skills to ace Bayesian Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Bayesian methods and Marketing Mix Models. Use specific examples that demonstrate your proficiency in Python and any relevant projects you've worked on.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Discuss how your skills align with the job requirements, particularly your experience with econometric modelling and data analysis.
Showcase Relevant Projects: If you have worked on projects involving Bayesian modelling or advanced data analysis, include these in your application. Describe your role, the tools you used, and the impact of your work.
Highlight Soft Skills: Don't forget to mention your communication skills and ability to collaborate with different teams. These are crucial for translating complex models into actionable insights, which is a key part of the job.
How to prepare for a job interview at ECM Talent
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python, particularly with libraries like pandas and NumPy. Highlight any projects where you've built Bayesian Marketing Mix Models or used probabilistic programming, as this will demonstrate your technical expertise.
✨Prepare for Scenario-Based Questions
Expect questions that require you to think critically about marketing strategies and data-driven decisions. Practice explaining how you would approach scenario-based simulations and the implications of your findings on marketing budget allocation.
✨Communicate Complex Concepts Clearly
Since strong communication skills are essential, practice explaining complex Bayesian models in simple terms. Be ready to discuss how your insights can translate into practical business outcomes, especially when collaborating with non-technical teams.
✨Demonstrate Your Problem-Solving Approach
Prepare examples of how you've validated and stress-tested models in the past. Discuss specific challenges you faced and how you identified opportunities for improvement, showcasing your analytical thinking and attention to detail.