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- Tasks: Join our team to enhance marketing effectiveness through data analysis and modelling.
- Company: Work with a top global company known for innovation and excellence in data science.
- Benefits: Enjoy remote work flexibility with just one day onsite per month and competitive pay.
- Why this job: Make a real impact on marketing strategies while developing your data science skills.
- Qualifications: Must have experience in Marketing Mix Models, PyMC, and strong statistical knowledge.
- 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.
Job Type: Contract/Temporary
Location: London (Remote/1 day onsite a month at Bankside)
Job Ref: BBBH160520_1747642728
Date Added: May 19th, 2025
Consultant: Louis Poynter
Duration: 6 Months Contract
Inside IR35
My client, a top global company, is seeking a Data Scientist with PyMC & MMM expertise to join their Data Science team. The role focuses on enhancing marketing effectiveness through Marketing Mix Modelling, Multi-Touch Attribution, and other models. Note: This position requires setup via an Umbrella Company/PAYE.
Responsibilities:
- Oversee data collection, extraction, manipulation, analysis, and validation.
- Analyze datasets to understand KPIs and prepare data for modelling.
- Utilize Excel, SQL, Python, Pandas for data processing, variable creation, and modelling.
- Build and refine base models, select variables, and interpret coefficients.
- Improve models and move towards finalization.
- Create sales effect/ROI workbooks, response curves, and optimization charts.
- Perform scenario analysis for budget allocation and forward-looking optimization.
- Validate models, identify weaknesses, and suggest improvements.
Requirements:
- Proven experience in developing and implementing Marketing Mix Models.
- Expertise in PyMC, Python, and familiarity with R for MMM.
- In-depth understanding of statistical modelling and ML techniques.
- Experience with regression models in MMM context.
- Solid experience with Probabilistic Programming and Bayesian Methods.
- Proficiency in mining large, complex datasets using SQL and Spark.
- Strong knowledge of statistical techniques and mathematical foundations.
- Working knowledge of Pymc, cloud data science frameworks, and Azure preferred.
- Deep expertise in areas like Optimization, Applied Mathematics, or Simulation.
Educational qualifications:
MS or PhD in Data Science, Computer Science, Applied Mathematics, Statistics, or related fields with modelling and computer science background are highly desirable.
Carbon60, Lorien & SRG - The Impellam Group STEM Portfolio are acting as an Employment Business regarding this vacancy.
Data Scientist with PyMC & Marketing Mixed Modelling experience employer: Lorien
Contact Detail:
Lorien Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist with PyMC & Marketing Mixed Modelling experience
✨Tip Number 1
Network with professionals in the data science and marketing fields. Attend industry meetups or webinars focused on Marketing Mix Modelling and PyMC to connect with potential colleagues and learn about the latest trends.
✨Tip Number 2
Showcase your practical experience with PyMC and Marketing Mix Modelling through personal projects or contributions to open-source projects. This hands-on experience can set you apart during interviews.
✨Tip Number 3
Prepare for technical interviews by brushing up on your knowledge of statistical modelling and machine learning techniques. Be ready to discuss how you've applied these concepts in real-world scenarios.
✨Tip Number 4
Familiarise yourself with the company's recent projects and case studies related to data science and marketing. This will help you tailor your discussions and demonstrate your genuine interest in their work.
We think you need these skills to ace Data Scientist with PyMC & Marketing Mixed Modelling experience
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with PyMC, Marketing Mix Modelling, and relevant programming skills like Python and SQL. Use specific examples to demonstrate your expertise in statistical modelling and data analysis.
Craft a Compelling Cover Letter: In your cover letter, explain why you are interested in this role and how your background aligns with the company's needs. Mention your experience with marketing effectiveness and any relevant projects you've worked on.
Showcase Relevant Projects: If you have worked on projects involving Marketing Mix Modelling or similar data science tasks, include them in your application. Describe your role, the tools you used, and the outcomes of these projects to illustrate your capabilities.
Highlight Educational Background: Since the position requires a strong educational foundation, ensure you clearly state your qualifications, such as your MS or PhD in Data Science or related fields. Emphasise any coursework or research that is particularly relevant to the job.
How to prepare for a job interview at Lorien
✨Showcase Your Technical Skills
Be prepared to discuss your experience with PyMC, Python, and SQL in detail. Bring examples of past projects where you successfully implemented Marketing Mix Models or used statistical techniques, as this will demonstrate your expertise.
✨Understand the Business Context
Research the company and its marketing strategies. Understanding how your role as a Data Scientist can enhance their marketing effectiveness will help you answer questions more effectively and show your genuine interest in the position.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving skills, especially related to budget allocation and model validation. Practise explaining your thought process and how you would approach real-world scenarios relevant to the role.
✨Ask Insightful Questions
Prepare thoughtful questions about the team dynamics, current projects, and the tools they use. This not only shows your enthusiasm but also helps you gauge if the company culture aligns with your values.
