Senior Data Scientist – Marketing Mix

Satori Analytics

. **What Your Day Might Look Like:** .

Posted 7/31/2026full-timeRemote • 🇬🇷 GreeceSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in Marketing Mix Modeling, regression analysis, and econometric techniques to drive data-informed decision-making. Proficient in Python or R, SQL, and various data science libraries, with a strong ability to communicate insights to both technical and non-technical stakeholders.

Highest-signal resume keywords

Marketing Mix ModelingRegression AnalysisPython or R SkillsSQL ProficiencyData Science Best Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Econometric ModelingTime-Series AnalysisStatistical InferenceFeature EngineeringModel ValidationBudget OptimizationSales ForecastingHypothesis TestingScenario PlanningIncrementality Testing

Soft Skills

Critical ThinkingClear CommunicationCollaborationMentoring

Tools & Technologies

SHAPPandasNumPyScikit-learnStatsmodelsSciPyGitAzureAWSGCP

Industry Keywords

ROIROASIncremental RevenueMarket ShareAdstockSaturationResponse CurvesBaseline DecompositionConstrained Mathematical OptimizationGenerative AI

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformNumpyPandasPySparkPythonScikit-LearnSparkSQL

About the role

Key responsibilities & impact

**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.

Requirements

What you’ll need

**Your Superpowers 🚀:**
- Strong professional experience in Data Science, Marketing Science, Econometrics, or Commercial/Advanced Analytics, delivering end-to-end modelling projects with clear business impact.
- Hands-on experience in one or more of: Marketing Mix Modeling, sales/demand forecasting, pricing & promotions analytics, econometric or time-series modelling, or budget/resource optimization.
- Solid grounding in regression, statistical inference, hypothesis testing, feature engineering, and model validation — plus working comfortably with trends, seasonality, and lagged effects.
- A critical eye: able to identify model limitations, challenge assumptions, and judge whether results are both statistically and commercially credible.
- Strong Python or R and SQL skills (joins, CTEs, window functions), with libraries such as pandas, NumPy, scikit-learn, statsmodels, or SciPy — and explainability tools like SHAP.
- Fluent in commercial concepts (ROI, ROAS, incremental revenue, margin, market share) and comfortable partnering directly with business-facing teams.
- Ability to independently structure and lead analytical workstreams, manage priorities, and communicate clearly with senior stakeholders.
- Experience with Git or another version-control system.
**Bonus points for:**
- Direct experience building or enhancing MMMs, and familiarity with adstock, saturation, response curves, baseline decomposition, and incrementality.
- Constrained mathematical optimization (SciPy Optimize, CVXPY, Pyomo) and MMM frameworks such as Google Meridian, Meta Robyn, or LightweightMMM.
- Causal inference, experiment design, geo-experiments, or incrementality testing; comfort with both Bayesian and frequentist approaches.
- Cloud and analytics platforms (Azure, AWS, GCP, Databricks, Microsoft Fabric, Snowflake) and exposure to MLflow, MLOps, or Spark/PySpark.
- Exposure to Generative AI or AI-assisted analytical workflows.

Benefits

Comp & perks

**Perks on Perks:**
- Competitive salary and hybrid work model – come hang out in our Athens office or work remotely from anywhere in European economic Area (EU, Switzerland etc.) or UK (up to 6 weeks per year).
- Training budget to level up your skills from the top tech partners in the market (Microsoft, AWS, Salesforce, Databricks etc.) – whether it’s certifications or courses, we’ve got you covered.
- Private insurance, top-tier tech gear, and the chance to work with a stellar crew.
Ready to create some data magic with us? Hit that apply button and let’s get started.