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Marketing Science · Tunis
Marketing Science Intern
Join our team and surround yourself with highly motivated and skilled coworkers to build cutting edge solutions for prestigious clients around the globe.
Topic 1: Exploratory Data Analysis automation
Description:
This project focuses on the critical role of pre-modeling 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 pre-modeling 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 problem-solving skills
Learning Outcomes:
Develop a structured approach to pre-modeling analysis in MMM Identify and automate key pre-modeling 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 client-specific 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 problem-solving 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 problem-solving skills
Learning Outcomes:
Assess the effectiveness of time-variant coefficients and Bayesian methods
Identify effective model selection criteria Balancing business/statistical relevance Model validation for bias and prediction power
Suggest enhancements/improvements
Job openings Role Intern Locations
About MASS Analytics
We specialize in Marketing Mix Modeling (MMM) and Media Effectiveness Measurement. We offer our clients a comprehensive MMM software suite backed up by a wide range of managed services solutions to help identify sales drivers, measure MROI and optimize Marketing budgets.
Marketing Science · Tunis
Marketing Science Intern
Join our team and surround yourself with highly motivated and skilled coworkers to build cutting edge solutions for prestigious clients around the globe.
Privacy policy for recruitment using Teamtailor
The service for handling recruitments and simplifying the hiring process (the "Service") is powered by Teamtailor on behalf of MASS Analytics ("Controller" “we” “us” etc.). It is important that the persons using the Service ("Users”) feel safe with, and are informed about, how we handle User's personal data in the recruitment process. We strive to maintain the highest possible standard regarding the protection of personal data. We process, manage, use, and protect User's Personal Data in accordance with this Privacy Policy ("Privacy Policy").
1. General
We are the controller in accordance with current privacy legislations. The Users’ personal data is processed with the purpose of managing and facilitating recruitment of employees to our business.
2. Collection of personal data
We are responsible for the processing of the personal data that the Users contributes to the Service, or for the personal data that we in other ways collects with regards to the Service.
When and how we collect personal data
make an application through the Service or otherwise, adding personal data about themselves either personally or by using a third-party source such as Facebook or LinkedIn; and use the Service to connect with our staff, adding personal data about themselves either personally or by using a third-party source such as Facebook or LinkedIn. provides identifiable data in the chat (provided through the website that uses the Service) and such data is of relevance to the application procedure;
We collect data from third parties, such as Facebook, Linkedin and through other public sources. This is referred to as “Sourcing” and be manually performed by our employees or automatically in the Service.
In some cases, existing employees can make recommendations about potential applicants. Such employees will add personal data about such potential applicants. In the cases where this is made, the potential applicant is considered a User in the context of this Privacy Policy and will be informed about the processing.
The types of personal data collected and processed
The categories of personal data that can be collected through the Service can be used to identify natural persons from names, e-mails, pictures and videos, information from Facebook and LinkedIn-accounts, answers to questions asked through the recruiting, titles, education and other information that the User or others have provided through the Service. Only data that is relevant for the recruitment process is collected and processed.
Purpose and lawfulness of processing
The purpose of the collecting and processing of personal data is to manage recruiting. The lawfulness of the processing of personal data is our legitimate interest to simplify and facilitate recruitment.
Personal data that is processed with the purpose of aggregated analysis or market research is always made unidentifiable. Such personal data cannot be used to identify a certain User. Thus, such data is not considered personal data.
The consent of the data subject
The User consents to the processing of its personal data with the purpose of Controller’s handling recruiting. The User consents that personal data is collected through the Service, when Users;
* make an application through the Service, adding personal data about themsel