Director - Process Data Enablement Product Owner

Job Description

Position Summary

The Director, Process Data Enablement Product Owner, is a key leadership role within the Launch Line Intelligence product line team. This individual will be responsible for defining the key capabilities, developing a roadmap and driving delivery for our process data and analytics products. This role will lead a team of product analysts and data engineers to deliver high-quality, reliable, and governed data products that enable Manufacturing Science & Technology and Commercialization customers to run Process Performance Monitoring (PPM), batch data investigations, and cross-site process robustness analyses. In addition, the role will be a key stakeholder in the development and adoption of Process Digital Twins

 

The Director will operate at an enterprise level, partnering with Technical Product Managers, S&T stakeholders, process data Subject Matter Experts, solution architects, data scientists, and cross-divisional IT teams to translate data needs into scalable, reusable, and compliant data engineering and analytics solutions.

 

Key Responsibilities

• Define and communicate the product vision and roadmap for process data enablement, ensuring alignment with business goals and user needs, particularly in support of process transfer and product launch activities.

• Lead, mentor, and develop a team of product analysts and data engineers, fostering a culture of innovation, collaboration, and excellence.

• Oversee the entire product lifecycle from conception to sustainment, including gathering and prioritizing product and customer requirements to support the product pipeline.

• Collaborate extensively with senior Science & Technology (S&T) stakeholders across all modalities (biologics, small molecule, vaccines) to ensure the successful delivery of data products and analytics solutions that meet their evolving needs.

• Drive the execution of data acquisition, data quality, and data governance to ensure the reliability and integrity of our data assets.

• Champion a data-driven culture by promoting data literacy and enabling self-service analytics across the organization.

• Manage the product backlog and prioritize initiatives based on business value and importance to key programs.

• Ensure all solutions are compliant with SDLC, GMP/GxP, and other regulatory requirements.

• Foster strong relationships with key partners, including manufacturing sites, Development and Commercialization teams, and across divisional and enterprise IT, to facilitate seamless process transfers and launches.

 

Required Qualifications

• A bachelor's degree in engineering, Computer Science, or a related field, with a minimum of 10 years of relevant experience.

• Proven experience in a product ownership or product management role, with a track record of successfully launching data-intensive products.

• Strong leadership skills with experience managing and mentoring teams of technical professionals.

• Excellent communication and interpersonal skills, with the ability to influence and negotiate with senior stakeholders.

• Strategic thinker with the ability to translate business needs into a compelling product vision.

• Knowledge of biopharmaceutical manufacturing processes and associated data systems (e.g., MES, LIMS, PI, SAP).

Preferred Qualifications

• Detailed Knowledge of Small Molecule API manufacturing processes

• Experience with agile methodologies and a digital and innovative mindset.

• Familiarity with advanced analytics, machine learning, and AI applications in a manufacturing context.

• Experience with data visualization tools such as OSI PI, Power BI, or Seeq.

• Background in manufacturing or process analytics (PPA/PPM), including cross site or ‑franchise level‑ analysis and GMP/GxP considerations.

• Experience with agile methodologies and a digital and innovative mindset

Required Skills:

Agile Methodology, Agile Methodology, Agile Transformation, Backlog Management, Business Case Development, Business Process Analysis, Commercialization, Computer Science, Da