At a Glance
- Tasks: Lead the development of Marketing Mix Models to drive retail sales insights.
- Company: Join Publicis Connected Media UK, a leader in innovative marketing solutions.
- Benefits: Enjoy competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborate with diverse teams in a dynamic and supportive environment.
- Why this job: Make a real impact by guiding investment decisions with data-driven insights.
- Qualifications: Experience in data analysis and econometrics is essential.
The predicted salary is between 50000 - 65000 £ per year.
Publicis Connected Media UK is seeking a Consultant Growth Scientist to lead the development of Marketing Mix Models that reveal drivers of retail sales and guide investment decisions. You will manage data preparation, refine model variables using econometric principles, and present actionable insights to clients and colleagues, working with cross-functional teams to enhance modelling approaches and data quality.
Growth Scientist: Marketing Mix Modelling employer: Publicis Groupe
Publicis Groupe is an exceptional employer, offering a dynamic work culture that fosters creativity and collaboration in the heart of Greater London. With a strong focus on employee growth, we provide comprehensive benefits such as Pension, Life Assurance, and Private Medical cover, ensuring our team feels valued and supported. Join us to lead innovative strategies for renowned brands like Disney, while enjoying a workplace that champions inclusivity and professional development.
StudySmarter Expert Advice🤫
We think this is how you could land Growth Scientist: Marketing Mix Modelling
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We think you need these skills to ace Growth Scientist: Marketing Mix Modelling
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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