Junior Data Scientist

Job Title: Junior Data Scientist (Bayesian Modeling & MMM)

Location: Toronto, ON (Hybrid)

Experience Level: Entry-Level

About WPP Media:

WPP is the creative transformation company. We use the power of creativity to build better futures for our people, planet, clients and communities. For more information, visit wpp.com.

WPP Media is WPP’s global media collective. In a world where media is everywhere and in everything, we bring the best platform, people, and partners together to create limitless opportunities for growth. For more information, visit wppmedia.com.

At WPP Media, we believe in the power of our culture and our people. It’s what elevates us to deliver exceptional experiences for both our clients and each other. In this role it will be critical to embrace WPP & WPP Media’s shared core values:

• Be Extraordinary by Leading Collectively to Inspire transformational Creativity.
• Create an Open environment by Balancing People and Client Experiences by Cultivating Trust.
• Lead Optimistically by Championing Growth and Development to Mobilize the Enterprise.

About the Role:

We are looking for a Junior Data Scientist with a strong foundation in Bayesian statistics and Marketing Mix Modeling (MMM) to join our analytics team. You will support the development of advanced models that drive data-informed decisions across marketing, customer analytics, and business strategy.

Key Responsibilities:

 • Develop and implement Bayesian statistical models for business and marketing use cases

 • Assist in building and maintaining Marketing Mix Models (MMM) to evaluate channel effectiveness

 • Perform data cleaning, feature engineering, and exploratory data analysis (EDA)

 • Support model validation, testing, and performance monitoring

 • Translate complex analytical results into clear business insights and recommendations

 • Collaborate with cross-functional teams including marketing, product, and finance

 • Create dashboards and visualizations using tools like Tableau, Power BI, or Python libraries

 • Document methodologies, assumptions, and model outputs for stakeholders

Required Qualifications:

 • Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Computer Science, or related field

 • Strong understanding of Bayesian statistics (priors, posteriors, inference techniques)

 • Familiarity with Marketing Mix Modeling (MMM) concepts and frameworks

 • Proficiency in Python or R (e.g., pandas, NumPy, PyMC, Stan, scikit-learn)

 • Knowledge of SQL for data extraction and manipulation&l