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 an Industrialized Assets Product Lead, you will be a leader within the Data Science Industrialization team charged with setting a vision for and championing 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 systems to simplify and fuel insight generation.

Role Responsibilities

• Lead the advancement of at scale “industrialized” data science capabilities and industrialized asset products
• Own vision and maintain a holistic and consistent roadmap for industrialized asset products
• 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 user community (100+ data scientists) 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
• Provide strategic and technical input for data science industrialization roadmap
• Provide input on platform evolution, vendor scan, and overall data science industrialization capability roadmap development
• Coordinate with other product and digital teams to ensure industrialized asset products fully enable and are integrated into the end to end data science and analytics service delivery ecosystem
• Partner with AIDA Data team to integrate industrialized assets into enterprise-level analytics data products where appropriate

Qualifications

Must-Have

• 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
• 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)

Nice-to-Have

• Advanced degree in Data Science, Computer Engineering, Computer Science, Information Systems or related discipline
• 2-3 years of hands-on product management experience
• Hands on experience working in Agile teams, processes, and practices
• Experience working in a cloud based analytics ecosystem (AWS, Snowflake, etc)
• Experience in software/product engineering
• 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

  
Work Location Assignment: Flexible

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