Applying ML/AI to real-world healthcare challenges—such as reducing costs and improving care quality.
Requirements
- Hands-on experience implementing machine learning solutions at scale in production environments.
- Experience with ML frameworks (e.g., Scikit-learn, PyTorch) and working with Client, business relevant datasets.
- Proficient in handling large datasets using Scala/Spark, Python, and leveraging cloud-based ML/AI tools.
- SQL proficiency (SQL Server, Oracle, Hive).
- Expertise in applying statistical methods to solve business problems and interpret outcomes.
- Solid SQL and programming experience in Python, R, and/or Scala.
- Experience with distributed frameworks such as Spark.
Responsibilities
- Develop, validate, and QA train ML models; collaborate with production operations for deployment and performance monitoring.
- Deliver end-to-end, value-driven solutions—covering data pipelines, model development, and user-facing applications.
- Perform exploratory data analysis to validate hypotheses for use cases.
- Partner with stakeholders (R&D, Operations, Product) to assess ML/AI-driven business opportunities and risks.
- Engage in knowledge-sharing and platform design sessions to advance the data science framework.
- Provide solutions targeting payment integrity, cost reduction in audits, and quality-of-care improvements.
- Document your methodology and results clearly to foster team collaboration.
Other
- 0-4 years of experience developing and deploying ML models in commercial or operational settings.
- Strong adaptability, confidentiality handling, task prioritization, and collaboration skills.
- Strong track record presenting data-driven insights across varying levels—from analysts to executives.
- Ability to operate effectively in dynamic, matrixed, or global team environments.
- Familiarity with health insurance models, managed care principles, coding standards, claims adjudication, and fraud/waste/abuse processes.
