Data Scientist – Intelligent Media Planning

Satori Analytics

Data Scientist focusing on intelligent media planning, building customer segmentation and media budget solutions at Satori Analytics. Collaborating on large-scale data processing and analysis in Athens, Greece.

Posted 7/10/2026full-timeAthens • 🇬🇷 GreeceMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

NumpyPandasPythonScikit-LearnSparkSQL

About the role

Key responsibilities & impact

Design and build customer segmentation solutions on payment transaction data, from feature engineering through clustering to business-ready segment narratives
Engineer behavioural features at scale — multi-window RFM, spend trajectory, recency decay, and time spine construction on transactional datasets
Validate and stress-test clusters using silhouette scores, stability testing, and — most importantly — business interpretability
Explain your models using SHAP, translating feature importance into plain-English segment stories for non-technical stakeholders
Calculate and interpret competitive metrics such as Spend Index (issuer view) and Wallet Share (merchant view), including a correct treatment of network coverage limitations
Build media planning logic — temporal disaggregation of coarse budgets into monthly plans (Denton-Cholette or equivalent), heuristic channel splits, and MMO response curve integration
Handle data filtering with rigour — BIN/ICA logic for issuer data, merchant_parent_name logic for merchant data
Document your work — methodology docs, assumption logs, and structured write-ups that others can pick up and run with

Requirements

What you’ll need

Strong Python — production-quality, clean, reproducible code (pandas, NumPy, scikit-learn)
Solid SQL — joins, window functions, and aggregations at scale
Applied statistics — distributions, regression, hypothesis testing, and model evaluation
Segmentation depth — K-Means, GMM, DBSCAN; you know the maths behind the algorithms, not just the API calls
Cluster validation instincts — silhouette score, stability testing, and an eye for whether a segmentation makes business sense
Feature engineering craft on transaction data — multi-window RFM, spend trajectories, recency decay
Explainability skills — comfortable with SHAP and able to turn feature importance into narratives clients understand
Git / version control as a matter of habit
Clear written communication — you document methodology and assumptions without being asked
Independence — you work from a defined brief, manage your own progress, and are proactive when blocked
AI awareness — you follow the space and know when (and when not) to reach for AI tooling.
**Bonus Points for:**
Card network or payment transaction data experience — familiarity with the quirks of issuer and merchant data
LLM API integration for persona and narrative generation (OpenAI, Anthropic)
UMAP / t-SNE for cluster visualisation
Spark or Dask for large-scale data processing
Prior work in financial services, retail, or loyalty analytics

Benefits

Comp & 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.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonSQLApplied StatisticsK-Means ClusteringGMMDBSCANFeature EngineeringCluster ValidationSHAPData Filtering

Soft Skills

Clear Written CommunicationIndependenceProactive Problem Solving