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Tier-1 employer, metro location, and mid-level generalist ML role drive high candidate competition.
Core ML and data science skills are highly transferable across industries despite helpful pharma domain knowledge.
Explicit 1–3 years requirement plus mandatory ML, Python, and degree filters enforce moderate strictness.
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Develop, maintain, and optimize a proprietary AI engine to support Copay optimization with machine learning models meeting SLA expectations.
Lead applied analytics and AI-driven automation projects by collaborating with global cross-functional and technical teams to translate business needs into technical specifications.
Create and deploy advanced machine learning, statistical analyses, intelligent dashboards, and automated workflows to extract insights and enhance decision-making in pharma value and access contexts.
Master’s degree in computer science, statistics, or STEM field; alternatively, Bachelor’s degree with 1 to 3 years relevant experience.
Proficiency in Python and experience with analytic tools like R or Python, including machine learning libraries such as TensorFlow, PyTorch, Scikit-learn.
Strong foundation in machine learning algorithms, statistical techniques such as hypothesis testing, regression, clustering and classification.
Work Experience Required: 1 to 3 years in a related data science or analytics role in STEM domain.
Experience applying machine learning and MLOps in healthcare or pharmaceutical data analytics, especially with copay or patient support services data.
Ability to work effectively with global, cross-functional technical teams and manage AI development lifecycle including model deployment and monitoring.
Skilled in building automated analytics pipelines leveraging cloud platforms (AWS, Azure, GCP) and tools like Databricks, Airflow, or Kubernetes.