Job Description
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Company Overview
The employer is a global healthcare and pharmaceutical organization focused on developing innovative medicines, diagnostics, and technology-enabled solutions. Its teams use advanced science, data, and artificial intelligence to support drug development, commercial strategy, and research initiatives.
Job Responsibilities
- Strategic Internal Consulting: Act as a dedicated advisor to Pharma Commercial and Product R&D teams on high-priority AI initiatives. Translate ambiguous business and product challenges into robust AI and machine learning strategies while ensuring technical solutions support commercial goals and development timelines.
- Platform Advocacy & Roadmap Influence: Promote the use of existing AI platform capabilities to maximize reuse and efficiency. Identify technological gaps across priority projects and communicate new requirements to platform engineering teams to help shape enterprise AI platform roadmaps.
- Healthcare Context & Compliance: Apply knowledge of the pharmaceutical and healthcare environment to consultative work. Ensure proposed AI architectures and machine learning solutions account for compliance, data privacy, and regulatory requirements, including GxP considerations.
- Stakeholder Management: Partner with senior leadership across Commercial and R&D to support strategic decisions. Translate complex algorithmic, generative AI, and compliance concepts into clear information for non-technical business stakeholders.
- Advisory Leadership: Guide the architectural and strategic direction of multiple end-to-end data science projects while supporting data scientists responsible for execution. Advise on advanced AI initiatives and emerging technical capabilities.
- Impact & Strategy: Lead the consultative phase of high-impact projects that can influence broader business strategy. Contribute to the organization’s AI vision, frameworks, and data science best practices.
- Accountability & Problem Solving: Address complex business and technical problems by developing customized analytical strategies. Advise teams on the robustness, scalability, and long-term maintainability of machine learning models in production.
- Business & Technical Ability: Apply advanced programming and data analysis expertise to advise on ambiguous business challenges, including situations where data ownership resides with execution teams.
Who You Are
- Master’s degree in Biostatistics, Computer Science, Data Science, or a related discipline required. PhD is strongly preferred.
- At least 7 years of direct, relevant experience in Data Science with significant exposure to AI and machine learning technologies.
- Consultative Track Record: Proven experience working with senior leadership to inform, influence, and advise on high-level strategic business decisions. Experience in an advisory capacity beyond direct execution is highly valued.
- Industry & Compliance Knowledge: Demonstrated familiarity with pharmaceutical or healthcare environments, including compliance, regulatory requirements such as GxP, and data privacy considerations associated with deploying AI in healthcare.
- Platform & Strategy Influence: Experience designing, promoting, and implementing enterprise-wide data science frameworks, platforms, and organizational best practices.
- Execution Advisory: Extensive experience guiding complex data science projects from end to end and driving measurable business impact through advisory and architectural leadership.
Technical Skills
- Programming & Analysis: Deep expertise and expert-level proficiency in multiple programming languages. Python is essential, while R is a plus. Strong capabilities in sophisticated data analysis are also required.
- AI/ML Expertise: In-depth understanding and hands-on experience with state-of-the-art machine learning, generative AI techniques, and experimental design.
- System Integration & Platform Architecture: Strong system integration experience and a deep understanding of generative AI processes. Ability to architect systems that dynamically retrieve information from diverse business sources while leveraging centralized enterprise platforms.
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