Senior Data Scientist

Mastery of Python for statistical analysis and modeling. Proficiency in SQL and handling large data volumes. Solid experience with data science libraries (pandas, numpy, scikit-learn, statsmodels, PySpark). Experience with Spark and/or distributed processing. Knowledge of inferential statistics, hypothesis testing, and multivariate analysis. Proven experience with predictive modeling and model performance evaluation (MSE, ROC, AUC, etc.). Mastery of data storytelling, translating complex technical insights into clear narratives for executive and non-technical audiences. Experience in MLOps, including GIT for code versioning, CI/CD pipelines, and model monitoring in production environments. Ability to translate business hypotheses into analytical solutions. Collaboration, curiosity, communication, and technical leadership skills.

Lead advanced analytical studies using statistical and Data Science methods to identify patterns, predict behaviors, and maximize business value. Translate complex business problems into rigorous analytical hypotheses and high-impact data-driven solutions. Design, develop, and operationalize predictive and prescriptive models (e.g., regression, classification, time series). Ensure model robustness and sustainability in production using MLOps best practices, model versioning, monitoring, and automated re-training. Collaborate with Data Engineering and Product Managers to optimize feature engineering pipelines and enable scalable, low-latency model application. Contribute to the evolution of analytical architecture and data/model governance by suggesting improvements. Lead innovation experiments and proofs of concept in applied AI, such as NLP, demand forecasting, anomaly detection, and recommendation systems. Act as a technical mentor, disseminating Data Science knowledge and best practices through workshops, code reviews, and complex project leadership. Foster an analytical culture by ensuring data-driven decision-making across the company.