Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Join our team as a Data Scientist to enhance marketing effectiveness through data analysis and modelling.
- Company: Work with a top global company known for innovation and excellence in the industry.
- Benefits: Enjoy remote work flexibility with one day onsite per month and competitive pay.
- Why this job: Make a real impact on marketing strategies while developing your skills in a collaborative environment.
- Qualifications: Expertise in PyMc, Python, and statistical modelling is essential; familiarity with R is a plus.
- Other info: This is a 6-month contract role requiring setup via an Umbrella Company/PAYE.
The predicted salary is between 48000 - 72000 £ per year.
Data Scientist with PyMc & Marketing Mixed Modelling experience
6 Months Contract
Inside IR35
Remote/1 day onsite a month
My client, a top Global company, are currently looking to recruit a Data Scientist with PyMc & MMM experience to join their team on a 6-month contract basis. Please note if successful this position will need to set up via an Umbrella Company/PAYE.
This Senior Data Scientist is required to work with our clients' Data Science team to drive Marketing Effectiveness using Marketing Mix Modelling, Multi-Touch Attribution, and other models.
Responsibilities:- Oversee and be responsible for data collection including data extraction and manipulation, data analysis and validation.
- Analyse all datasets to ensure that each KPI is understood, and data is ready for modelling.
- Proficiency in using Excel/SQL/Python/Pandas to process, transform, create variables, and build models.
- Build base models according to the project specification, incorporating all drivers of KPIs, providing rationale for variables selection, understanding coefficients and contributions.
- Taking base models, oversee or build in additional improvements and progress the model towards finalisation.
- Create sales effect/ROI workbook.
- Create response curves and optimisation charts.
- Budget allocation. Run scenarios required to answer client objectives for the purpose of forward-looking optimization.
- Validate models, identify areas of weakness, suggest and test possible improvements and ensure robustness and validity.
- Proven experience in developing and implementing Marketing Mix Models.
- Be an expert in PyMc, Python and familiar with R programming for MMM Models.
- Have in-depth understanding of statistical modelling / ML techniques.
- Experience with Regression based models applied to the context of MMM modelling.
- Solid experience with Probabilistic Programming and Bayesian Methods.
- Be an expert in mining large & very complex data sets using SQL and Spark.
- Have in-depth understanding of statistical modelling techniques and their mathematical foundations.
- Have a good working knowledge of Pymc and cloud-based data science frameworks and toolkits. Working knowledge of Azure is preferred.
- Have a deep knowledge of a sufficiently broad area of technical specialism (Optimisation, Applied Mathematics, Simulation).
Contact Detail:
Lorien Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Marketing Mixed Modelling Data Scientist
✨Tip Number 1
Network with professionals in the data science and marketing fields. Attend industry meetups or webinars where you can connect with others who have experience in Marketing Mixed Modelling. This can lead to valuable insights and potential referrals.
✨Tip Number 2
Showcase your expertise in PyMc and other relevant tools by engaging in online communities or forums. Contributing to discussions or sharing your projects can help you stand out as a knowledgeable candidate.
✨Tip Number 3
Prepare for potential interviews by brushing up on your understanding of statistical modelling techniques and their applications in marketing. Be ready to discuss specific examples of how you've used these methods in past projects.
✨Tip Number 4
Familiarise yourself with the latest trends in marketing analytics and data science. Being able to discuss current developments and how they relate to Marketing Mix Modelling will demonstrate your passion and commitment to the field.
We think you need these skills to ace Marketing Mixed Modelling Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Marketing Mix Modelling, PyMc, and relevant programming languages like Python and SQL. Use specific examples to demonstrate your skills in data extraction, manipulation, and analysis.
Craft a Compelling Cover Letter: In your cover letter, explain why you are the perfect fit for the role. Mention your expertise in statistical modelling and how it aligns with the company's goals. Be sure to include your understanding of marketing effectiveness and your approach to data-driven decision making.
Showcase Relevant Projects: If you have worked on projects involving Marketing Mix Modelling or similar data science tasks, summarise these experiences. Highlight the tools you used, the challenges you faced, and the outcomes of your work to demonstrate your capability.
Proofread Your Application: Before submitting, carefully proofread your application materials. Check for any spelling or grammatical errors, and ensure that all information is clear and concise. A polished application reflects your attention to detail, which is crucial for a data scientist.
How to prepare for a job interview at Lorien
✨Showcase Your Technical Skills
Make sure to highlight your proficiency in PyMc, Python, and SQL during the interview. Be prepared to discuss specific projects where you've applied these skills, especially in the context of Marketing Mix Modelling.
✨Understand the Business Context
Demonstrate your understanding of how data science drives marketing effectiveness. Be ready to explain how your models can impact business decisions and improve ROI for clients.
✨Prepare for Scenario-Based Questions
Expect questions that ask you to solve hypothetical problems related to budget allocation or model validation. Practise explaining your thought process clearly and logically, as this will showcase your analytical skills.
✨Discuss Your Experience with Data Validation
Be prepared to talk about your experience in data collection, extraction, and validation. Highlight any specific techniques you've used to ensure data integrity and readiness for modelling.
