Apply

Ready to go for it?

AI Apply speeds things up—apply directly if you prefer.

FREE ACCESS
5,000–10,000 jobs/day
JobTailor Logo

See all jobs on JobTailor

Search thousands of fresh jobs every day.

Discover
  • Fresh listings
  • Fast filters
  • No subscription required
Create a free account and start exploring right away.
Satori Analytics

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

Core Competencies

Role fit
Core Competencies

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

Demonstrates expertise in designing customer segmentation solutions using payment transaction data, with strong capabilities in feature engineering, clustering algorithms, and model explainability. Proficient in Python and SQL, with a solid understanding of applied statistics and cluster validation techniques.

Highest-signal resume keywords
Python ProgrammingSQL ProficiencyApplied StatisticsSegmentation TechniquesFeature Engineering

ATS Keywords

Tailor your resume
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills
PythonSQLApplied StatisticsK-Means ClusteringGMMDBSCANFeature EngineeringCluster ValidationSHAPData Filtering
Soft Skills
Clear Written CommunicationIndependenceProactive Problem Solving
Tools & Technologies
GitSparkDaskUMAPT-SNE
Industry Keywords
Payment Transaction DataCustomer SegmentationFinancial ServicesRetailLoyalty Analytics

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.