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Tier-1 employer, mid-level data science role in metro with a popular title increases applicant competition.
Role requires pharma-specific knowledge (US copay ecosystem) and ML expertise, limiting cross-industry transferability.
Requires explicit years, pharma domain knowledge, and ML skills creating moderately strict shortlisting criteria.
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Develop, deploy, and manage advanced machine learning and statistical models including a proprietary AI engine optimizing Copay and GTN initiatives with SLA adherence.
Collaborate globally to roadmap and implement AI-driven analytics, automated workflows, and dashboards enhancing commercial and development decision-making.
Perform exploratory and targeted data analyses using machine learning, hypothesis testing, and data mining techniques to uncover actionable insights from large US healthcare datasets.
Master’s degree in computer science, statistics or STEM with 2+ years of information systems experience OR Bachelor’s degree with 4+ years experience.
Experience with analytic tools/languages like Python or R and foundation in machine learning algorithms and statistical techniques including regression, clustering, classification.
Foundational understanding of US pharmaceutical ecosystem, patient support services (Copay), and data types such as claims and prescription data.
Work Experience Required: 2-4 years in relevant information systems/data science roles.
Experienced operating in US pharma analytics environment with exposure to patient support and commercial data use cases such as Copay and GTN.
Proficient in implementing and maintaining AI/ML model pipelines including MLOps and DevOps practices with cloud platforms (AWS, Azure, Google Cloud) and tools (Docker, Kubernetes).
Able to translate business needs into technical specifications and drive AI-enabled automation and insight generation across global, cross-functional teams.