Marketing Science Intern
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Job Description
Topic 1:Exploratory Data Analysis automation
Description:
This project focuses on the critical role of premodeling analysis in MMM and explores ways to automate this phase for efficiency. Exploratory data analysis ensures that data is clean relevant and optimized for modeling significantly impacting the quality and reliability of the final model results. This project aims to identify key premodeling steps such as data exploration transformation and validation and to automate them where possible.
Key attributes / Main competencies:
Experience with Spark & SQL is a plus
- Experience with common data science toolkits (R Python etc) is a plus
- Excel and PowerPoint skills required
Analytical and problemsolving skills
Learning Outcomes:
- Develop a structured approach to premodeling analysis in MMM
- Identify and automate key premodeling steps for improved efficiency and accuracy
- Improve modelling insights and recommendation
Topic 2:Improve Reporting Capabilities
Description:
This project focuses on enhancing the effectiveness and customization of reporting dashboards in MMM. The goal is to design a flexible dashboard architecture that can be reused and adapted to clientspecific needs reducing the time spent on data checks while maintaining data quality. Additionally exploring the automation of PowerPoint presentation creation through Power BI will streamline the reporting process.
Key attributes / Main competencies:
- Dashboard architecture and design
- Power BI automation and presentation generation
- Data quality assurance and efficiency optimization
Learning Outcomes:
- Develop a customizable reusable dashboard for efficient data checking
- Automate the generation of presentations from MMM dashboards
Improve process efficiency without sacrificing data quality
Topic 3:Build MMM Models WITH Hierarchical Modelling
Description:
This project explores alternative model estimation techniques to improve model quality in MMM. It focuses on evaluating the impact of hierarchical modelling for regional segmentation. This investigation aims to determine how these techniques can improve the quality of the built models.
Key attributes / Main competencies:
- Understanding of data mining machine learning and statistical models including linear & logistic regression PCA clustering etc
- Experience with common data science toolkits (R Python etc) is a plus
- Excel and PowerPoint skills required
- Analytical and problemsolving skills
Learning Outcomes:
- Evaluate hierarchical modelling benefits for segmented markets
- Identify effective model selection criteria
- Balancing business/statistical relevance
- Model validation for bias and prediction power
- Suggest enhancements/improvements
Topic 4: Build MMM Models with Bayesian Regression
Description:
This project explores alternative model estimation techniques to improve model quality in MMM. It focuses on assessing the benefits of the Bayesian regression approach to leverage priors and other domain knowledge obtained from experimentation or other analytics studies.
Key attributes / Main competencies:
- Understanding of data mining machine learning and statistical models including linear & logistic regression PCA clustering etc
- Experience with common data science toolkits (R Python etc) is a plus
- Excel and PowerPoint skills required
- Analytical and problemsolving skills
Learning Outcomes:
- Assess the effectiveness of timevariant coefficients and Bayesian methods
- Identify effective model selection criteria
- Balancing business/statistical relevance
- Model validation for bias and prediction power
- Suggest enhancements/improvements
Employment Type
Full Time
