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- 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 dynamic environment.
- Qualifications: Expertise in PyMc, Python, and statistical modelling is essential; experience with SQL and Spark is a plus.
- Other info: This is a 6-month contract role requiring setup via an Umbrella Company/PAYE.
The predicted salary is between 43200 - 72000 £ per year.
A Data Scientist with PyMc & Marketing Mixed Modelling experience is required for a 6-month contract. This position is inside IR35 and can be remote with 1 day onsite a month in Bankside.
My client, a top global company, is looking to recruit a Data Scientist to join their team. The Senior Data Scientist will work with the 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 variable 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.
- Run scenarios required to answer client objectives for the purpose of forward-looking optimisation.
- Validate models, identify areas of weakness, suggest and test possible improvements and ensure robustness and validity.
Requirements:
- Proven experience in developing and implementing Marketing Mix Models.
- Expertise in PyMc, Python, and familiarity with R programming for MMM Models.
- 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.
- Expertise in mining large and very complex data sets using SQL and Spark.
- In-depth understanding of statistical modelling techniques and their mathematical foundations.
- Good working knowledge of Pymc and cloud-based data science frameworks and toolkits; working knowledge of Azure is preferred.
- 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 relevant webinars, workshops, or meetups to connect with others who have experience in Marketing Mixed Modelling. This can lead to valuable insights and potential referrals.
✨Tip Number 2
Familiarise yourself with the latest trends and tools in Marketing Mix Modelling and Bayesian methods. Follow industry leaders on social media and read up on recent case studies to demonstrate your knowledge during interviews.
✨Tip Number 3
Prepare to discuss specific projects where you've successfully implemented Marketing Mix Models. Be ready to explain your thought process, the challenges you faced, and how you overcame them, as this will showcase your practical experience.
✨Tip Number 4
Consider creating a portfolio that highlights your work with PyMc, Python, and SQL. Include examples of models you've built and the impact they had on marketing effectiveness, as this can set you apart from other candidates.
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 analysis and model building.
Craft a Compelling Cover Letter: In your cover letter, explain why you are interested in the role and how your background aligns with the responsibilities outlined in the job description. Mention your experience with statistical modelling 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, be sure to include these in your application. Describe your role, the tools you used, and the outcomes of the projects.
Highlight Technical Skills: Clearly list your technical skills related to the job, such as proficiency in Excel, SQL, Python, and any experience with cloud-based frameworks like Azure. This will help demonstrate your capability to handle the technical demands of the role.
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 work can impact KPIs and contribute to the overall marketing strategy of the company.
✨Prepare for Problem-Solving Questions
Expect questions that assess your analytical thinking and problem-solving abilities. Practice explaining your thought process when faced with complex datasets or modelling challenges, as this will showcase your expertise.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's current projects and challenges in Marketing Mix Modelling. This shows your genuine interest in the role and helps you understand how you can contribute effectively.
