Data Scientist

We are looking for a Senior Data Scientist with a solid statistical background and practical experience in data science projects in business environments. The candidate must be able to frame complex problems as modeling problems, execute rigorous research cycles, and deliver reproducible solutions that can be integrated by engineering teams. This role operates in close collaboration with an ML Engineering team. The candidate is expected to have clear judgment about their responsibilities within that ecosystem and the discipline to work with software engineering standards, not just analysis standards. What We Are Looking For Technical Fundamentals 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). Statistical Rigor 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. Development Discipline 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. Judgment on AI Responsible use of generative AI tools as assistants: with the critical ability to review and validate what they produce. Judgment to evaluate when agent systems or LLMs are the right tool and when they are not. Recommended Experience Notes for the Search The selection process includes a practical technical evaluation and review of the candidate’s previous work. Reasoning ability and judgment will be valued over code production speed. We are not looking for profiles who use tools without understanding them: we are looking for candidates who can justify their technical and statistical decisions. The candidate will work under engineering standards defined by the team — willingness and ability to adopt them from the start of any project is expected. 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). Academic Background Master’s or Doctoral degree in: Mathematics, Statistics, Actuarial Science, Physics, Computer Science, or related fields. Experience in academic or applied research is a differentiator. Lo que ofrecemos