Lead Data Scientist | Helios Enterprise Data Warehouse | Full Time

GENERAL SUMMARY:

Lead Data Scientist, Healthcare Analytics works with system leaders, business stake holders and business data analysts to fully understand their needs for data science solutions. Lead Data Scientist architects, defines, and design the data science solution, and works with Data Scientist, Associate Data Scientist, and other business analysts to implement the data science solution. Works/collaborates with various teams to build the solution and implement the solution in operations. Lead Data Scientist, Healthcare Analytics develops cutting edge solutions to resolve complex business problems. Skilled in using statistical techniques and good at solution architecting, design and implementing the solution. Hands on in using a variety of common analytical and AI tools. Will present findings in a compelling manner to system leaders, technical and non-technical audience. The position requires a team lead mindset who is ready to lead and deliver the projects using agile methodology.

PRINCIPLE DUTIES AND RESPONSIBILITIES:

• Architects, constructs, and design easy and expandable solution to a given problem.
• Converts a one-liner definition of a problem into a workable solution.
• Works independently with leaders/stakeholders to understand the complex requirements and be able to design/present a solution.
• Leads and guides team members to develop data science programs, algorithms.
• Able to multi-task and be able to prioritize projects pipe-line based on business needs.
• Able to access structured or non-structured data from internal or external data sources.
• Leverages appropriate data science techniques to drive the creation of insights from data.
• Combines data extraction, preparation, and data cleansing with model training and evaluation.
• Stays current on data science and analytics tools, techniques, and practices.
• Able to implement swift ML/NLP/LLM solutions.
• Works on an Agile (Scrum) project team.
• Guides the team as appropriate during the project.
• Deploys machine learning models in cloud environments, specifically Microsoft Azure, ensuring scalability and robustness.
• Monitors and evaluates the performance of deployed models, ensuring they meet business needs and comply with governance standards.
• Implements and manages automated processes for model retraining, validation, and monitoring in production environments.
• Engages in continuous assessment and review of deployed models, providing feedback.