Design, train, validate, and deploy machine learning and deep learning models in production environments with big data.
Implement advanced anomaly detection and pattern recognition techniques to identify irregularities, fraud, operational risks, or atypical behavior in the data.
Execute A/B testing and statistical experimentation to validate hypotheses, measure impact, and optimize information analysis products.
Collaborate with cross-functional teams (product, engineering, business, tax/accounting) to translate needs into data science use cases.
Ensure data quality through pipeline cleaning, validation, orchestration, and monitoring processes.
Develop and maintain technical documentation, metrics dashboards, and model performance reports.
Propose new solutions based on predictive models, advanced analytics, and generative AI techniques that add strategic value.

Bachelor's degree in Systems Engineering, Mathematics, Statistics, Computer Science, or related field (Master's/Doctorate desirable).
6–12 years of experience in data science, with at least 3 years leading projects in production.
Solid experience in supervised learning, A/B testing, anomaly detection, and pattern recognition.
Experience putting ML/DL models with millions of records or transactions into production.
Languages: Python (required), R, and SQL (advanced).
Experience with ML pipelines, MLOps, and cloud deployment (AWS, GCP, or Azure).
Knowledge of ML/DL frameworks (scikit-learn, TensorFlow, PyTorch).
Experience with anomaly detection (Isolation Forest, LOF, autoencoders, Prophet, ARIMA, robust statistics).
Experience in pattern recognition and predictive modeling (clustering, time series, sequences, recurrent neural networks).
SQL and NoSQL databases; experience with vector databases (Pinecone, pgvector, Milvus).
Strong data visualization skills (Matplotlib, Seaborn, Plotly, Power BI, Tableau).
Experience with model testing and cross-validation.

Senior Data Scientist

Key skills