Data Scientist with Python and SQL expertise

- Take ownership of end-to-end problem solving from defining issues and exploring data to developing models and evaluating business impact. - Transform analytical results into concise, actionable recommendations that guide product choices, pricing plans, and operational enhancements. - Plan and assess controlled experiments (A/B testing), including hypothesis formation, metric outlining, and statistical analysis. - Examine large healthcare and transactional datasets to discover patterns, boost patient adherence, and improve user engagement. - Create machine learning solutions involving feature engineering, model creation, and training workflows, collaborating with engineering teams for deployment and operation within production. - Work extensively with large datasets across relational and distributed data systems, refining access patterns for analytics and model creation. - Implement and comply with security best practices and HIPAA regulations for managing Protected Health Information (PHI). - Conduct statistical evaluations to assess model performance, supporting decision-making for product and operational strategies. - Leverage GitHub and GitHub Actions for version control and CI/CD of data science processes. - Contribute to model deployment strategies (e.g., batch scoring, API-driven inference, or event-based processing) in conjunction with engineering teams to facilitate production use. - Set up monitoring for model performance, data shifts, and overall system health, ensuring clear feedback mechanisms for retraining and ongoing enhancements. - Partner with engineering teams to guarantee models function smoothly within production systems, understanding distributed system factors like retries, idempotency, and error handling. - Follow Agile methodologies in a dynamic startup environment.