About Getnet Getnet is the leading fintech provider of payment solutions in Latin America and Iberia. Getnet is built to serve merchants, ISVs, PayFacs, orchestrators, platforms and the next generation of commerce enablers, connecting technologies and markets through a unified, omnichannel payment experience that boosts performance and drives growth. ¿At Getnet, your work connects to something bigger, creating a real impact across integral teams and markets for our clients and communities. Design, train and deploy scalable machine learning models using Getnet data to solve high-impact payment and business challenges across geographies.

Build and maintain end-to-end machine learning pipelines, from exploration and feature engineering through scoring, deployment and monitoring. Drive decisions on model architecture, experimentation and deployment, balancing performance, scalability, explainability and maintainability. Partner with business, product and cross-functional teams to turn prioritized opportunities into production-ready data solutions.

Strengthen engineering quality through clean code, automated testing, documentation, version control, CI/CD and reusable standards. Monitor production models for drift, data quality, performance and reliability, using alerts and dashboards to guide action. Mentor junior colleagues and communicate complex findings clearly to non-technical stakeholders across markets. 5+ years of professional experience in Machine Learning Engineering, Data Science or Applied Research roles. (Proven experience building, deploying and operating end-to-end machine learning solutions on tabular data at scale in production cloud environments. (Experience leading technical decisions, proofs of concept or AI/ML initiatives and supporting junior team members. (Experience working in agile,



cross-functional teams across multiple markets or geographies. (Education Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, Physics, Engineering or a related quantitative field. (PhD in a related quantitative field. (English, professional proficiency. (Python and core data and machine learning libraries, including pandas, NumPy, scikit-learn, XGBoost or LightGBM, SciPy and statsmodels. (Applied statistics, probability and linear algebra, with experience in classification, regression, uplift and anomaly detection at scale, feature engineering, temporal validation, explainability (XAI) and mathematical optimization. (Observational causal inference, uplift modeling, A/B testing and experimental design, including familiarity with EconML, causalml and DoWhy. (Azure Databricks, including Workflows, Delta Lake, Unity Catalog and Databricks Connect; Azure OpenAI Service, AKS and Key Vault; PySpark, Spark SQL and MLflow for tracking, registry, serving and versioning. (Time-series and model-monitoring techniques, including change point detection, clustering, seasonality and trend analysis, drift detection, data quality alerts and performance dashboards. (Transformer and LLM concepts, prompt engineering and the integration and operationalization of LLM-based solutions through Azure OpenAI or similar services. (Git, CI/CD for machine learning, pytest, schema validation and clean, testable, well-documented Python code. (AWS services, including SageMaker, S3, Bedrock, EKS, Lambda, Step Functions and Glue, and experience working across Azure and AWS. (Ability to mentor junior colleagues and foster continuous improvement. (We care for your well-being, so you will have health insurance, life insurance, dental insurance, annual health care, and access to Wellhub.

We promote flexibility with a hybrid and collaborative model;

📌 Machine Learning Engineer, Senior (León)
🏢 Doist
📍 León