Lead the design, development, validation, and evolution of predictive and prescriptive models (classification, regression, time series, recommendation, anomaly detection) applied to agribusiness and company operations.
Conduct exploratory data analysis (EDA), feature design, and integration of multiple sources (ERP, sensors, machine telemetry, weather data, external datasets), ensuring quality, consistency, and governance.
Structure and maintain data and model pipelines together with Data Engineering, including preprocessing, training, validation, deployment, and monitoring in a cloud environment.
Work on MLOps, defining best practices for versioning, performance monitoring, drift detection, and periodic review of models in production.
Translate business problems into hypotheses, experiments, and quantitative analyses, estimate impact, and communicate results clearly to technical and executive audiences.
Support the development of the Data & AI roadmap by proposing new use cases, proofs of concept, and improvements to existing solutions.

Education: Bachelor's degree in Statistics, Computer Science, Engineering, Mathematics, Physics, Quantitative Economics, or related fields; a postgraduate degree or Master's in Data Science, AI, or related areas is a plus.
Strong experience in Data Science and Machine Learning.
Proficiency in Python and data libraries (Pandas, scikit-learn, etc.).
Experience with SQL and handling large volumes of data.
Experience in building and deploying predictive models.
Knowledge of feature engineering, model validation, and evaluation metrics.
Experience with cloud environments (AWS, Azure, or GCP).
Experience with cloud computing environments (Azure, AWS, or GCP) and orchestration/automation tools (e.g., Databricks, Airflow, cloud jobs, CI/CD pipelines).
Strong communication skills with technical teams and business stakeholders.

Senior Data Scientist

Key skills