Director, Product – Data & Insights

Director of Data, Insights & Customer Experience

At Thermo Fisher's PPD clinical research business, we're using digital innovation, data science, and AI to reimagine how life-changing therapies reach patients. Our teams combine deep scientific expertise with advanced analytics, automation, and digital platforms to make research smarter, faster, and more connected.

We know that innovation happens when diverse minds meet. Our Digital Science, Data, and AI professionals collaborate closely with scientists, clinicians, and operational experts to solve real-world challenges in clinical research. Alongside our partnership with Open AI, you can be part of the collaboration that will help to improve the speed and success of drug development, enabling customers to get medicines to patients faster and more cost effectively.

You'll join a culture that values experimentation, learning, and collaboration — where your ideas can help shape how we deliver life-saving solutions and improve global health outcomes. Whether you're a data engineer, product manager, software developer, or AI scientist, you'll find opportunities here to apply your skills to work that truly matters — improving global health outcomes.

This is a remote based position for qualified candidates in the US (East Coast preference).

• Define and drive the data strategy for the Insights and Customer Experience digital product domain within PPD, ensuring alignment with product roadmaps and business objectives.
• Partner with Product Managers to translate product requirements into scalable data models and structures.
• Establish data as a core product asset, ensuring it is intentionally designed—not an afterthought.
• Define conceptual, logical, and physical data models to support digital product capabilities.
• Collaborate closely with data engineers and architects to implement scalable, high-quality data solutions.
• Ensure consistency between product data models and upstream source systems.
• Own data quality standards, monitoring, and remediation processes
• Establish processes to ensure data in digital products accurately reflects source systems.
• Drive initiatives that improve confidence in data across stakeholders ("single version of the truth")
• Implement validation, reconciliation, and auditing practices.
• Lead the development and adoption of clear, consistent data definitions and business logic.
• Ensure alignment across teams on key metrics, KPIs, and data usage.
• Establish and maintain data governance frameworks, including metadata management and lineage.
• Champion data literacy and shared understanding across the organization
• Lead and develop a team of data analysts supporting digital products.
• Oversee the design and development of dashboards and data visualizations that support business processes and decision-making.
• Ensure dashboards are intuitive, performant, and aligned to standardized definitions and trusted data sources.
• Partner with product and business teams to embed insights directly into workflows and digital experiences.
• Establish best practices for visualization, usability, and front-end data presentation.
• Enable AI and machine learning use cases by ensuring high-quality, well-structured, and accessible data.
• Partner with AI/ML teams to define data requirements for models and features.
• Ensure data readiness for training, inference, and continuous improvement of AI systems.
• Serve as a key partner to Product, Engineering, Architecture, and Business stakeholders across PPD.
• Influence decision-making through data clarity, governance, and strategic direction.
• Drive alignment across functions on data priorities and investments.

• Product teams within Insights and Customer Experience domain trust and rely on data without constant validation.
• Clear, consistent definitions exist for all critical metrics and data fields.
• Dashboards and data interfaces are widely adopted and drive business processes and decisions.
• Data discrepancies between systems are proactively identified and resolved.
• Scalable data models support current and future product needs.
• AI initiatives are accelerated by well-prepared, high-quality data.