Data Science and Analysis Specialist

Autopay Global is the newest member of the Autopay family, aiming to expand the reach of the group’s state-of-the-art payment integration and payment data technologies to the international market, providing seamless integration with local PSPs, support for multiple currencies and compliance with local frameworks.

• 5+ years in data science, analytics, or quantitative analysis with experience deploying models to production
• Proficiency in Python (pandas, scikit-learn, XGBoost/LightGBM) and SQL (BigQuery, Spark SQL, or equivalent)
• Experience building CLV, segmentation, propensity scoring, or recommendation models in a product or marketing context
• Solid statistical foundations: A/B testing, hypothesis testing, regression, causal inference, and experiment design
• Experience with feature stores or ML feature engineering pipelines; understanding of point-in-time correctness
• Ability to communicate analytical findings to non-technical stakeholders through dashboards, reports, and data storytelling
• Understanding of data governance, PII handling, and consent requirements in analytical workflows
• Growth mindset and ability to apply new technologies, patterns or solutions to the current workstream (i.e. GenAI-assisted work)

Nice to have:

• Experience with Google Vertex AI
• Familiarity with real-time segmentation and next-best-action decisioning systems
• Marketing mix modeling
• Background in payments, fintech, e-commerce, or marketplace analytics

• Build and maintain CLV, churn prediction, conversion propensity, and upsell/cross-sell models using the platform’s feature store and Customer 360 data
• Develop dynamic segmentation pipelines that produce real-time and batch audience outputs for personalization, CRM, and paid media activation
• Design and operate A/B testing and experimentation frameworks: define KPIs, calculate sample sizes, run statistical tests, and present actionable results
• Create and maintain analytical dashboards and reports for Product, Marketing, and Leadership stakeholders
• Collaborate with Data Engineers to define feature requirements, data quality expectations, and new data product specifications for the feature store
• Partner with AI Engineers on model evaluation datasets, retrieval relevance metrics, and experiment tracking for AI-driven personalization
• Monitor model performance in production: track prediction accuracy, calibration, fairness, and drift
• Produce audience and segment definitions consumed by downstream activation systems; ensure compliance with consent, PII, and governance policies
• Conduct deep-dive analyses on customer behavior, payment patterns, affiliate performance, and marketing attribution to generate commercial insights
• Contribute to data governance by defining business logic documentation, metric definitions, and data quality SLAs for analytical outputs