bluesight - Data Scientist II

Remote - United States1w ago

Remote Mid NA Cloud Computing Artificial Intelligence Data Scientist Learning & Development KPI Tracking Flask Python FastAPI AWS Git SQL Inventory Management Documentation

Requirements

• 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 • 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 • 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 • Master’s degree or PhD in math and/or statistics centric field of study • Experience on a data team of a similar size (

Responsibilities

• 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.