Lead the end-to-end development of data science solutions, from design through deployment and monitoring;
Design and build scalable data pipelines for ingestion, processing and feature engineering (batch and near real-time);
Develop, train and validate Machine Learning models (classification, regression, survival analysis, recommendation, among others);
Define and implement strategies for model versioning, reproducibility and governance;
Build and maintain APIs and services to serve models in production;
Monitor model performance (drift, degradation, business metrics) and propose continuous improvements;
Collaborate with business teams to translate problems into analytical solutions and deliver measurable impact;
Lead technical discussions, support architectural decisions and promote best practices across the team;
Mentor more junior data scientists, fostering the team’s technical growth;
Ensure technical documentation and clear communication with both technical and non-technical stakeholders.

Solid experience in Data Science / Machine Learning in production environments;
Advanced proficiency in Python (pandas, scikit-learn, numpy, etc.);
Experience building data pipelines and handling large volumes (Spark/PySpark or equivalent);
Strong SQL skills and data modeling experience;
Practical experience deploying models (APIs, batch jobs, microservices);
Knowledge of cloud platforms (AWS, GCP or Azure), including data and ML services;
Experience with code and model versioning (Git, MLflow or similar);
Strong background in statistics and model evaluation;
Ability to work autonomously and lead initiatives end-to-end;
Communication skills for interacting with business stakeholders and technical teams.

Differentials:

Senior Data Scientist – Machine Learning, Data Science

Key skills