Senior Manager, Industrialized Assets Product Lead

ROLE SUMMARY

Do you want to make an impact on patient health around the world? Do you thrive in a fast-paced environment that brings together scientific, clinical and commercial domains through engineering, data science, and analytics? Then join Pfizer Digital’s Artificial Intelligence, Data, and Analytics organization (AIDA) where you can leverage cutting-edge technology to inform critical business decisions and improve customer experiences for our patients and physicians. Our collection of engineering, data science, and analytics professionals are at the forefront of Pfizer’s transformation into a digitally driven organization leveraging data science and advanced analytics to change patients’ lives. The Industrialization team within Enterprise Data Science and Advanced Analytics leads the scaling of data and insights capabilities - critical drivers and enablers of Pfizer’s digital transformation.

As a Senior Manager, Industrialized Assets Product Lead, you will be a leader within the Data Science Industrialization team charged with setting a vision for and championing AI/analytic industrialized asset products that serve a broad base of users ranging from analysts, data scientists, and engineers to marketers, medical experts, and researchers. You will create, own the roadmaps for, and lead the development of an array of products that generalize point solutions into analytic and AI systems to simplify and fuel insight generation.

ROLE RESPONSIBILITIES

• Partner with other leaders within the AI and Data Science Industrialization team to define team roadmap and drive impact by providing strategic and technical input including platform evolution, vendor scan, and new capability development Communicate value delivered through industrialized AI/ML assets to end user functions (e.g., Chief Marketing Office, PBG Commercial and Medical Affairs) and partner to ideate, design, and implement industrialized assets that can be scaled across markets, brands, and TAs
• Partner with AIDA Data team to integrate industrialized assets into enterprise-level analytics data products where appropriate
• Partner with AIDA Platforms team on continuous development and end to end capability integration between OOB platforms and internal engineered components (API registry, ML library / workflow management, enterprise connectors, GenAI reusable components); Performance and resource optimization of managed pipelines and models
• Lead the advancement of at scale “industrialized” AI and data science capabilities and industrialized asset products
• Own vision and maintain a holistic and consistent roadmap for industrialized asset products
• Participate in the development of the product and service roadmaps for Generative AI products. Serve as product owner on Generative AI projects that deliver on the product roadmaps and mature the product and service offering across the enterprise.
• Provide the voice of the user as the product owner on agile product development teams to maximize value and impact of industrialized asset products
• Drive adoption of products through enterprise wide user community engagement (e.g., roadshows, training sessions, knowledge sharing etc.)
• Develop and maintain business-facing assets documentation/communication for industrialized asset products (e.g., value proposition, catalog, and adoption metrics)
• Define, monitor, and achieve product success and value metrics
• Coordinate with other product and digital teams to ensure industrialized asset products fully enable and are integrated into the end to end data science, analytics and AI service delivery ecosystem

BASIC QUALIFICATIONS

• Bachelor’s degree in analytics related area (Data Science, Computer Engineering, Computer Science, Information Systems, Engineering, or a related discipline)
• 7+ years of work experience in data science, analytics, engineering, or product management for a diverse range of projects
• 2-3 years of hands-on product management experience
• 2+ years of hands-on software development experience, preferably in a cloud environment such as AWS
• Track record of managing cross-functional stakeholder groups and effecting change
• Clearly articulates expectations, capabilities, and action plans; actively listens with others’ frame of reference in mind; appropriately shares information with team; favorably influences people without direct authority
• Clearly articulates scope and deliverables of projects; breaks complex initiatives into detailed component parts and sequences actions appropriately; develops action plans and monitors progress independently; designs success criteria and uses them to track outcomes; drives implementation of recommendations when appropriate, engages with stakeholders throughout to ensure buy-in
• Manages projects with and through others; shares responsibility and credit; develops self and others through teamwork; comfortable providing guidance and sharing expertise with others to help them develop their skills and perform at their best; helps others take appropriate risks; communicates frequently with team members earning respect and trust ofthe team
• Experience in translating business priorities and vision into product/platform thinking, set clear directives to a group of team members with diverse skillsets, while providing functional & technical guidance and SME support
• Demonstrated experience interfacing with internal and external teams to develop innovative data science solutions
• Strong business analysis, product design, and product management skills
• Deep expertise with data science enabling technology, such as Dataiku Data Science Studio, AWS SageMaker, or other data science platforms
• Strong hands-on skills in analytics engineering and data science (e.g., Python, R, SQL, industrialized ETL software)
• Experience working with various types of data (structured / unstructured)
• Deep understanding of MLOps principles and tech stack (e.g. MLFlow)
• Highly self-motivated to deliver both independently and with strong team collaboration
• Ability to creatively take on new challenges and work outside comfort zone
• Strong English communication skills (written & verbal)

PREFERRED QUALIFICATIONS

• Advanced degree in Data Science, Computer Engineering, Computer Science, Information Systems or related discipline
• Hands on experience working in Agile teams, processes, and practices
• Experience in developing Machine Learning based products, preferably with Large Language Models and GenAI solutions
• Experience in developing and operating analytic workflows and model pipelines that are parametrized, automated and reusable
• Experience developing and deploying data and analytic products for use by technical and non-technical audiences
• Pharma & Life Science commercial functional knowledge
• Pharma & Life Science commercial data literacy
• Experience with Dataiku Data Science Studio

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