More than 5 years in data science, statistical analysis, or applied research roles. Documentable end-to-end projects: from problem definition to delivery of a validated model. Experience working with engineering teams (ml engineers, data engineers) in agile environments. Work history in real code repositories (a shareable portfolio will be valued). Advanced mastery of python as the primary and sole development language. Solid knowledge of data science and ml libraries: scikit-learn, xgboost, lightgbm, pandas, polars, statsmodels, scipy. Experience with deep learning models ( tensorflow or pytorch ) when the problem justifies it. Ability to work with data at scale: advanced sql, pyspark for exploration and transformation. Access to and handling of data in cloud environments (gcs, azure blob storage). Experimental design and hypothesis testing applied to business problems. Understanding of causality: not just correlation but the ability to distinguish and apply appropriate techniques. Robust model validation: beyond accuracy, business metrics, bias analysis, and subgroup behavior. Professional use of git as part of the usual workflow, not as a formality at delivery time. Organized and modular python code: the candidate must produce deliverable code, not just exploration notebooks. Familiarity with experiment tracking tools (mlflow or equivalent) for experiment traceability. Ability to document models in a structured way: what it solves, with what data, with what limitations. Experience working under team standards: secure credential handling, data versioning, project structure.