Senior Data Scientist (Marketing Mix)

Are you passionate about AI? 🤖

At Satori Analytics, we aim to change the world one algorithm at a time by bringing clarity to global brands thought Data & AI. From cloud-based ecosystems for fintech to predictive models for airlines, our cutting-edge solutions cover the entire data lifecycle—from ingestion to AI applications.

As a fast-growing scale-up, our team of 100+ tech specialists—including Data Engineers, Data Scientists, and more—delivers innovative analytics solutions across industries like FMCG, retail, manufacturing and FSI. Join us as we lead the data revolution in South-Eastern Europe and beyond!

We are looking for a Senior Data Scientist to join our Data Science team and play a key role in marketing science, marketing effectiveness, and investment optimization projects.

You will work with marketing, media, sales, pricing, promotional, and external market data to help leading organisations understand what drives performance, measure the impact of marketing activities, and make better investment decisions.

This role is well suited to candidates with experience in Marketing Mix Modeling, marketing effectiveness, econometrics, forecasting, commercial analytics, or optimization. Deep expertise in every MMM technique is not required, but you should have the statistical foundation, modelling experience, and business understanding needed to lead complex analytical projects.

What Your Day Might Look Like:

• Build the models: Develop and enhance Marketing Mix Models to estimate the impact of media, promotions, pricing, seasonality, and other business drivers on performance.
• Quantify what matters: Apply regression, time-series, econometric, and ML techniques to measure incremental impact — modelling carryover, saturation, diminishing returns, and response curves.
• Optimise the spend: Develop scenario-planning and optimization approaches to guide media budget allocation and investment decisions.
• Interrogate the results: Evaluate assumptions, uncertainty, and business plausibility rather than relying on statistical fit alone, using SHAP, diagnostics, and sensitivity analysis to explain the drivers.
• Tell the story: Translate outputs into clear recommendations on channel performance, ROI, and budget strategy for both technical and non-technical audiences.
• Partner across the business: Work with Marketing, Commercial, Finance, BI, and Data Engineering to define questions, KPIs, and success criteria, and to operationalise clean, reproducible workflows.
• Raise the bar: Support less-experienced colleagues and contribute to reusable methodologies and Data Science best practices.