Finance Data Science Engineer

Lead the design, development, and deployment of advanced forecasting models using state-of-the-art libraries (e.g., statsforecast, Darts, skforecast, sklearn). Oversee end‑to‑end machine learning projects and pipelines from experimentation to production, ensuring scalability, reliability, and adherence to MLOps standards. Conduct robust accuracy assessments and ongoing performance monitoring of forecasting methodologies. Build, refine, and maintain high-quality Python codebases aligned with best software engineering practices. Explore data sets to uncover insights, define hypotheses, and answer complex business questions. 5-10 years of experience in data science, applied machine learning, forecasting, or a related field. BSc/MSc in data science, data engineering, computer science, mathematics, engineering, or an equivalent field. A PhD is a plus. Deep expertise in Python, statistical modeling, forecasting techniques, and modern data science libraries. Strong experience deploying machine learning solutions in cloud environments (AWS or Azure), ideally with MLOps tooling. Demonstrated ability to work with large data sets and data engineering tools. Proven track record delivering high-impact analytical solutions in a business environment. Strong communication skills with experience influencing senior stakeholders. Fluency in English required; Spanish is a plus. Autonomy, creativity, and the ability to drive decisions and propose new ideas. Flexibility to adapt scope to evolving business needs. Collaborate with data engineering teams to ensure strong data foundations, including data quality, metadata management, lineage, and effective governance. Facilitate the adoption of data science solutions through documentation, training, and change management.