Senior Data Scientist, Bluesight

• Degree or equivalent years of experience in data science, statistics, computer science, or similar quantitative field
• Ability to develop and productionize code in Python to serve API endpoints (e.g. Flask, FastAPI)
• A willingness to leverage genAI tools to assist with rapid experimentation
• Deep understanding of statistical models including distributions and transformations. Ability to create and evaluate supervised and unsupervised machine learning models (tree-based models, clustering) in a practical/applied context
• Hands-on experience with AWS cloud services including SageMaker, Athena, RDS, Glue, ECR, IAM, and CodeBuild
• Strong knowledge of Git workflows and version control best practices
• Master-level understanding of SQL
• Prior track record of a customer-friendly approach in presenting information and analysis for non-technical audiences
• Excellent verbal and written communication skills with an ability to advocate technical solutions to engineering teams as well as business stakeholders

Desirable

• Bluesight is looking for a Senior Data Scientist to join our central data team and help us drive the success of data science and analytics efforts across the organization.
• This role will work closely with external and internal stakeholders to deliver value through all stages of the RnD life cycle: identifying opportunities, building core infrastructure, running predictive models, analyzing impact, and highlighting improvement areas.
• This person will partner with cross-functional peers to strengthen Bluesight’s data culture and help us innovate within our suite of Medication Intelligence products
• Develop and productionize machine learning models for a range of healthcare products, including monitoring controlled substance handling and patient privacy, and inventory management
• Define and track KPIs to measure model performance and business impact
• Collaborate with product teams and customers to scope analytics projects and build prototypes. Translate ambiguous business problems into well defined, quantitative, and testable hypotheses
• Communicate technical findings to both technical and non-technical stakeholders, including customers. Present results and recommendations in customer-facing meetings
• Inform product roadmaps, evaluate technical feasibility, and assess project risks
• Quickly ramp up on new healthcare data sources as products evolve and adapt to new ML techniques and tools as needed for product requirements
• Raise the skill level of the entire analytics team by modeling strong research communication through team presentations and documentation. Drive collaboration through code reviews, experimental design feedback, and participation in model evaluation discussions