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# Data Science Data Science Data Modeler

- **Company:** Super
- **Location:** Spain
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Data Analysis, SQL Databases, Snowflake, Information Technology
- **Published:** July 17, 2026
- **Apply:** https://es.trabajo.org/oferta-5354-408a346c6f5e8fd3a30e5b0ad3bafc21

## About the Role

validation (Snowflake environment). Demonstrated track record of producing decision memos, strategy recommendations, or business cases that have measurably influenced product and commercial strategy. Ability to transform complex causal analyses into clear, compelling narratives for VP and C-suite audiences, making evidence accessible and actionable. Strong commercial and product acumen - an understanding of how data connects to business outcomes, product design, and customer behaviour. Minimum 5 years of relevant experience in analytics, data science, or product data science roles (ideally in product or commercial domains), with direct influence on strategy and growth through causal analysis and experimentation. Degree in Data Science, Statistics, Economics, Computer Science, Engineering, Mathematics, or a related quantitative field - or equivalent professional experience. Medical / Health Insurance Training & Learning Development We are a global technology group, dedicated to

## Description

memos with quantified impact ranges and clear "so what" guidance for senior stakeholders. Shape Strategy with Data Influence product roadmaps, commercial tactics, and marketing strategies by framing the right questions and designing experiments, including A/B tests and quasi-experiments. Design KPI trees, define metric intent, and specify requirements for Analytics Engineers to implement certified, production-grade data products. Define tracking plans for instrumentation, specify dashboard requirements and acceptance criteria, and ensure data capture supports decision-making needs. Collaborate with Product Managers, Commercial leaders, Analytics Engineers, and Marketers, leading problem-framing sessions and challenging assumptions. A/B testing, confidence intervals, regression, quasi-experimental designs (difference-in-differences, synthetic controls, regression discontinuity), and familiarity with CUPED and variance reduction techniques. Proficiency in SQL for exploratory analysis and data


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