Back
Mid-level, popular Data Scientist role in metro locations with broad skills and strong employer brand.
Requires finance domain knowledge, SAP/CIM familiarity, and forecasting expertise, limiting cross-industry transferability.
Explicit years plus mandatory production ML, Databricks, Python, and SQL/PySpark requirements.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Develop and maintain production-grade data science and automated data pipelines within Global Finance, focusing on predictive financial intelligence and forecasting.
Translate business problems into analytical models using machine learning, statistical techniques, and develop Power BI dashboards for actionable finance insights.
Collaborate globally across Data Scientists, Engineers, Finance Analysts, and ML Engineers to deploy and enhance analytical models and solutions in production monthly.
At least 4 years of experience in data science, data engineering, or related quantitative field.
Bachelor’s or Master’s degree in Engineering, Computer Science, Data Science, Business Analytics, Finance, Industrial Engineering, Statistics, Mathematics, or related STEM discipline.
Strong experience in production-grade Python coding, data pipeline development, and data analysis with SQL, PySpark specifically with finance ERP systems like SAP S/4HANA.
Experience with cloud platforms (Databricks, AWS or Azure), Power BI dashboard development, and deploying analytical models to production.
Proven expertise operating in finance or commercial environments, delivering data science/engineering solutions driving finance business insights.
Experienced in managing end-to-end data science lifecycle: from proofs of concept through production deployment with scalable, maintainable pipelines.
Comfortable working in distributed global teams using agile methodologies, able to communicate complex analytics clearly to senior stakeholders.