Galgo is a fast-growing fintech specialized in motorcycle sales and financing.
We are a purpose-driven company, the first and only financial institution focused on the underbanked sector in Chile, Mexico, and Colombia... 
And now, we’re very excited to be expanding into Brazil! 🚀

At Galgo, we work to create better opportunities for integration and development through financial access—helping people achieve their dreams.That’s why we’re looking for our next Sr. Data Scientist to join a high-performing team. The main responsibilities will be:

- Design, train, and deploy predictive models and advanced algorithms to automate key business processes and improve data-driven decision-making.

- Lead data science projects end-to-end, defining analytical objectives aligned with business needs and ensuring measurable impact.

- Develop robust, reproducible, and scalable machine learning pipelines in collaboration with engineering and data teams.

- Validate and monitor model performance in production, proposing adjustments and continuous improvements based on key metrics and evolving operational conditions.

What we’re looking for:

- 3+ years of experience as a Data Scientist, Machine Learning Engineer, or similar role.

- Proven experience managing end-to-end ML processes, from design to deployment.

- Advanced proficiency in Python and SQL.

- Strong knowledge of scikit-learn and boosting algorithms (XGBoost, CatBoost, LightGBM, etc.).

- Experience in cloud environments (GCP, AWS, or Azure).

- Hands-on experience implementing ML pipelines in production using frameworks like MLflow, Vertex AI, SageMaker, or similar.

- Solid understanding of model evaluation under class imbalance, with metrics such as AUC, precision-recall, F1-score, etc.

- Strong stakeholder management skills, with the ability to translate business needs into effective technical solutions.

- Nice to have experience in Fraud and/or Risk teams.

At Galgo, we are committed to attracting the best talent without distinctions, because we believe diversity is the most valuable asset of our organization.