Data Scientist

About the role:

We're looking for a driven Data Scientist passionate about understanding and shaping consumer demand. You'll go beyond traditional forecasting—taking existing demand forecasts and building what-if simulation models to quantify how pricing, promotions, and other commercial levers affect demand and business outcomes. Your work will directly empower strategic decisions, helping teams anticipate the future and simulate scenarios that maximize value and minimize risk.

If you're eager to shine, take ownership, and let the excellence of your work speak for itself, this is the place for you.

What You'll Do

Model demand dynamics: Use historical data and existing forecasts to simulate the impact of promotions, pricing strategies, and marketing actions on future demand.

Run what-if analyses: Design and execute scenario models to test how changes in price, discount depth, timing, or promotional mechanics could influence demand and margin performance.

Integrate insights: Combine statistical and machine learning approaches to quantify uplift and elasticity across diverse product categories and stores.

Enhance forecast intelligence: Partner with the forecasting team to enrich forecasts with promotion and pricing effects, ensuring improved accuracy and business relevance.

Validate and stress-test: Evaluate the robustness of what-if models under different market conditions and validate against real-world results.

Ensure data reliability: Guarantee the quality, consistency, and integrity of the datasets and pipelines powering your models.

Deploy & scale: Implement scalable, automated workflows to deploy, monitor, and retrain simulation models in production environments.

Experience & Expertise

• 3+ years of professional experience as a Data Scientist or in a related quantitative field
• 2+ years of proven experience in promotion impact analysis, what-if models, and price modeling
• Hands-on experience developing and deploying ML models in Python
• Exposure to what-if modeling, elasticity estimation, and causal inference techniques (e.g., uplift modeling, treatment/control analysis, counterfactual simulation) is a strong plus
• Solid understanding of statistical modeling, time series forecasting, and simulation frameworks
• Intermediate proficiency in technical English is desirable

Technical Skills

• Strong SQL skills and comfort working with large, complex datasets
• Solid experience with at least one major cloud platform (AWS or GCP)
• Familiarity with MLOps practices, CI/CD, and containerization tools (e.g., Docker) is a plus
• Exposure to microservices and distributed systems for scalable model deployment

Mindset & Ways of Working

• Ability to take ownership, deliver consistently, and meet commitments with autonomy
• Strong commitment to best practices in coding, testing, and documentation

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