Data Scientist

We are looking for a Data Scientist to build and maintain models that quantify the impact of promotions, media investment, pricing, and competitive activity on sales and market performance. The role sits at the intersection of marketing analytics, econometrics, and data science and is critical to informing commercial decisions across the Belgian market.

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The successful candidate will own the end-to-end modelling workflow: from extracting and joining large datasets in the data warehouse, to feature engineering and model selection, to explaining results to business stakeholders and embedding the work in reproducible analytical pipelines.

Key responsibilities

• Develop and maintain promotional lift, media impact, and sales response models using weekly and monthly commercial data.
• Quantify the incremental sales impact of:
• promotions and discount mechanics,
• media investment across channels,
• pricing and trade levers,
• competitor activity and share of voice.
• Build robust modelling datasets from on-prem data warehouse sources and external reference files.
• Apply a mix of regression, time-series, and causal/experiment-based approaches to estimate effect sizes, seasonality, and channel interactions.
• Work in a reproducible environment using Python, SQL, and version-controlled project workflows.
• Select and validate models using business-appropriate metrics and statistical guardrails.
• Translate modelling results into clear recommendations for marketing, sales, and finance teams.
• Collaborate with commercial stakeholders to define KPIs, scenario analysis, and decision support.

Required skills and experience

• Strong background in applied statistics, econometrics, or marketing mix modelling.
• Experience building models for sales, promotion, pricing, or media response.
• Proficiency in Python for analysis and modelling, particularly with pandas, numpy, scikit-learn, statsmodels, and data validation workflows.
• Strong SQL skills for working with large warehouse datasets and building analytical features.
• Experience with time-series and panel data analysis, including seasonality, trend decomposition, and lagged effects.
• Ability to work with business data where confounding, market noise, and imperfect measurement are common.
• Solid understanding of model validation, feature engineering, and practical interpretation of statistical outputs.
• Excellent communication skills and ability to explain analytical findings to non-technical stakeholders.

Preferred experience

• Experience in FMCG, retail, media, or consumer marketing analytics.
• Exposure to promotion planning, media mix, or sales response modelling.
• Knowledge of Bayesian modelling, hierarchical models, or causal inference methods is a strong advantage.
• Experience with experiment design, uplift analysis, or scenario planning.
• Familiarity with Jupyter, CI/testing practices, and production-quality data science workflows.

Education and profile

• Master's degree or equivalent practical experience in Data Science, Statistics, Economics, Quantitative Marketing, Mathematics, or a related field.
• A strong analytical mindset and a practical understanding of how modelling drives business decisions.

What success looks like in this role

• The candidate can independently design and maintain a promotion and media impact model that is statistically defensible and business-relevant.
• They can turn complex analytical findings into actionable commercial recommendations. xlxgzvr
• They can operate in a structured, reproducible, and well-documented data science environment.