> Markdown version of [/jobs/ext/2264829-senior-data-scientist-f500-expert](https://www.wearedevelopers.com/jobs/ext/2264829-senior-data-scientist-f500-expert). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist - F500 Expert - **Company:** Mercor - **Location:** Berlin, Germany (Remote available) - **Experience:** Expert - **Salary:** €124,800.0 - €145,600.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Data Governance, Data Infrastructure, R (Programming Language), Python (Programming Language), Machine Learning, Power BI, Azure Machine Learning, SQL Databases, Tableau (Software), Enterprise Data Management, Snowflake, Model Validation, Machine Learning Operations, Databricks - **Published:** August 27, 2026 - **Apply:** https://www.adzuna.de/details/5856680351 ## About the Role Must-Have * 5+ years working as a data scientist, analytics leader, or ML engineer at a Fortune 500 technology or enterprise organization. * Direct ownership of F500 data products, analytics initiatives, or machine learning systems in production. * Fluency in enterprise data science tooling and methodologies, with understanding of F500 data governance, privacy compliance, and cross-functional stakeholder alignment. Preferred * Prior rubric, technical curriculum, or model documentation authorship. ## Description * Construct enterprise data science scenarios for large-scale predictive modeling, multi-stakeholder analytics governance, and complex data infrastructure decisions at Fortune 500 accounts. * Build analytics tasks across F500 machine learning model development, enterprise data pipelines, and business intelligence at scale. * Develop data and MLOps scenarios using tools such as Snowflake, Databricks, Python/R, SQL, Tableau/Power BI, and enterprise ML platforms like SageMaker, Vertex AI, and MLflow. * Apply enterprise data science methodologies including statistical rigor, A/B testing frameworks, model validation, and MLOps best practices to produce reference analyses and executive-level insights. * Author rubrics that distinguish authentic enterprise data science judgment from generic textbook or tutorial-level recall.