As a Data Science and AI Technical Product Owner at Pfizer Digital's Artificial Intelligence, Data, and Analytics organization (AIDA), you will play a crucial role in leading the Industrialization team within the AI and Data Science COE. Your responsibilities include:
Partnering with other leaders in the AI and Data Science Industrialization team to define the team's roadmap and provide strategic and technical input
Communicating the value delivered through industrialized AI/ML assets to end-user functions and collaborating to design and implement scalable assets across markets and brands
Integrating industrialized assets into enterprise-level analytics data products in partnership with the AIDA Data team
Collaborating with the AIDA Platforms team on continuous development and integration between OOB platforms and internal engineered components
Leading the advancement of at-scale industrialized AI and data science capabilities and products
Owning and maintaining a holistic roadmap for industrialized asset products
Participating in the development of product and service roadmaps for Generative AI products and serving as a product owner on related projects
Driving adoption of products through enterprise-wide user community engagement and developing business-facing assets documentation/communication
Defining, monitoring, and achieving product success and value metrics
Basic qualifications for this role include a Bachelor's degree in an analytics-related area and 7 years of work experience in data science, analytics, engineering, or product management. You should also have experience in hands-on product management and software development, as well as a track record of managing cross-functional stakeholder groups and effecting change. Additionally, you should possess strong communication skills, project management abilities, and expertise in data science enabling technologies. Preferred qualifications include an advanced degree in a related discipline, hands-on experience working in Agile teams, and experience developing Machine Learning-based products. Additionally, experience in Pharma & Life Science commercial functional knowledge and data literacy is beneficial. As a Data Science and AI Technical Product Owner at Pfizer Digital's Artificial Intelligence, Data, and Analytics organization (AIDA), you will play a crucial role in leading the Industrialization team within the AI and Data Science COE. Your responsibilities include:
Partnering with other leaders in the AI and Data Science Industrialization team to define the team's roadmap and provide strategic and technical input
Communicating the value delivered through industrialized AI/ML assets to end-user functions and collaborating to design and implement scalable assets across markets and brands
Integrating industrialized assets into enterprise-level analytics data products in partnership with the AIDA Data team
Collaborating with the AIDA Platforms team on continuous development and integration between OOB platforms and internal engineered components
Leading the advancement of at-scale industrialized AI and data science capabilities and products
Owning and maintaining a holistic roadmap for industrialized asset products
Participating in the development of product and service roadmaps for Generative AI products and serving as a product owner on related projects
Driving adoption of products through enterprise-wide user community engagement and developing business-facing assets documentation/communication
Defining, monitoring, and achieving product success and value metrics
Basic qualifications for this role include a Bachelor's degree in an analytics-related area and 7 years of work experience in data science, analytics, engineering, or product management. You should also have experience in hands-on product management and software development, as well as a track record of managing cross-functional stakeholder groups and effecting change. Additionally, you should possess strong communication skills, project management abilities, and expertise in data science enabling technologies. Preferred qualifications include an advanced degree in a related discipline, hands-on experience working in Agile teams, and experience developing Machine Learning-based products. Additionally, experience in Pharma & Life Science commercial functional knowledge and data literacy is beneficial.