Data Scientist, Data & Analytics - Customer, Loyalty & Marketing

About the Role

Key Responsibilities

• Develop and maintain Marketing Mix Models to measure effectiveness of TV, radio, digital, out-of-home, print, and in-store promotions.
• Translate complex statistical outputs into clear, actionable insights for Marketing, Finance, and Leadership teams.
• Build in-house models using Python, R, or other languages, conduct scenario simulations, and optimize budget allocation.
• Monitor data pipelines and ensure data quality and granularity, including sales, media spend, promotions, weather, seasonality, and store traffic.
• Contribute to broader analytics strategy, including incrementality testing, geo-experiments, and attribution modeling.
• Facilitate best practice sharing and contribute to automation and improvement of existing processes.
• Present actionable insights and recommendations to marketing business managers to influence strategies and enhance loyalty programs.

Requirements

• Bachelor's or Master's degree in a quantitative field such as Statistics, Data Science, Economics, Applied Mathematics, or Computer Science.
• 3+ years of experience in Marketing Mix Modeling, econometrics, or media effectiveness analysis.
• Strong background in statistics and econometrics, including linear regression, time series, and Bayesian models.
• Proficiency in data wrangling and visualization tools like Pandas, SQL, Power BI, or Tableau.
• Experience with query processing languages such as SQL and PySpark, and familiarity with cloud platforms like Azure.
• Excellent communication skills to articulate technical information clearly and collaborate across teams.
• Strong stakeholder management skills and experience engaging with diverse teams and senior leaders.
• Collaborative, proactive, and able to drive projects to successful completion.
• Adaptable to new technologies and business needs, with a curiosity for continuous learning.

Nice to Have

• Experience with scenario simulations and budget optimization.
• Hands-on experience with cloud-based platforms such as Azure.