Job Description

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 Digitalxe2x80x99s Artificial Intelligence, Data, and Advanced 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 Pfizerxe2x80x99s transformation into a digitally driven organization that leverages data science and advanced analytics to change patientsxe2x80x99 lives. The Data Science Industrialization team within Data Science Solutions and Initiatives leads the scaling of data and insights capabilities - critical drivers and enablers of Pfizerxe2x80x99s digital transformation.As the AI and Data Science Production Deployment Lead, you will be a leader within the Data Science Industrialization team charged with driving the deployment of AI use cases and reusable components into full production. You will lead a global team and partner with cross-functional business stakeholders and Digital leaders to catalyze identification, design, iterative development, and continuous improvements of deployment processes to support production data science workflows and AI applications. Your team will define and implement standard processes for quality assurance, testing, data ops, model ops, and dev ops while also providing SDLC, support, platform engineering, and cloud engineering guidance as needed. In addition, you will be responsible for providing critical input into the AI ecosystem and platform strategy to promote self-service, drive productization and collaboration, and foster innovation. Your team will be accountable to key Pfizer business functions (including Pfizer Biopharma, R&D, PGS, Oncology, and Enabling Functions) for production deployments of data science workflows and AI solutions that support major business objectives across all of Pfizerxe2x80x99s core business units.ROLE RESPONSIBILITIESLead deployment of production AI solutions and reusable software components with automated self-monitoring QA/QC processesImplement QA and testing, data ops, model ops, and DevOps for data science workflow products, industrialized workflow accelerators, and best practices in the production deployment of scalable AI/ML analytic insights productsEnforce best practices for QA and testing and SDLC production support to ensure reliability and availability of deployed softwareAct as a subject matter expert for production deployment processes of data science workflows, AI solutions, and reusable software components on cross functional teams in bespoke organizational initiatives by providing thought leadership and execution supportDirect QA and testing, data ops and model ops, DevOps, platform and cloud engineering research, advance data science workflow CI/CD orchestration capabilities, drive improvements in automation and self-service production deployment processes, implement best practices, and contribute to the broader talent building framework by facilitating related trainingsSet a vision, prioritize workstreams, and provide day-to-day leadership, supervision, and mentorship for a global team with technical & functional expertise that includes QA and testing, DevOps, data science, and operationsCoach direct reports to adopt best practices, improve technical skills, develop an innovative mindset, and achieve professional growth through technical and organizational thought leadershipCommunicate value delivered through reusable AI components to end user functions (e.g., Chief Marketing Office, Biopharma Commercial and Medical Affairs) and evangelize innovative ideas of reusable & scalable development approaches/frameworks/methodologies to enable new ways of developing and deploying AI solutionsPartner with other leaders within the 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 developmentPartner with AI use case development teams to ensure successful integration of reusable components into production AI solutionsPartner with AIDA Platforms team on end to end capability integration between enterprise platforms and internally developed reusable component accelerators (API registry, ML library / workflow management, enterprise connectors)Partner with AIDA Platforms team to define best practices for production deployment of reusable components to identify and mitigate potential risks related to component performance, security, responsible AI, and resource utilizationBASIC QUALIFICATIONSBachelorxe2x80x99s degree in AI, data science, or engineering related area (Computer Engineering, Computer Science, Information Systems, Engineering or a related discipline)10+ years of work experience in data science, or engineering, or operations for a diverse range of projects2-3 years of hands-on experience leading data science or AI/ML deployment and operations teamsTrack record of managing stakeholder groups and effecting changeRecognized by peers as an expert in production deployment and AI/ML ops with deep expertise in CI/CD and DevOps for monitoring and orchestration of data science workflows, and hands-on developmentUnderstands how to synthesize facts and information from varied data sources, both new and pre-existing, into clear insights and perspectives that can be understood by business stakeholdersClearly articulates expectations, capabilities, and action plans; actively listens with othersxe2x80x99 frame of reference in mind; appropriately shares information with team; favorably influences people without direct authorityClearly 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; engages with stakeholders throughout to ensure buy-inManages 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 of the teamExperience 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 supportAbility to manage projects from end-to-end, from requirements gathering through implementation, hypercare, and development of support processes to ensure longevity of solutionsDemonstrated experience interfacing with internal and external teams to develop innovative data science solutionsStrong understanding of data science development lifecycle (CRISP)Deep experience with CI/CD integration (e.g. GitHub, GitHub Actions or Jenkins)Deep understanding of MLOps principles and tech stack (e.g. MLFlow)Experience working in a cloud based analytics ecosystem (AWS, Snowflake, etc)Highly self-motivated to deliver both independently and with strong team collaborationAbility to creatively take on new challenges and work outside comfort zoneStrong English communication skills (written & verbal)PREFERRED QUALIFICATIONSAdvanced degree in Data Science, Computer Engineering, Computer Science, Information Systems or related disciplineExperience in solution architecture & designExperience in software/product engineeringStrong hands-on skills for data and machine learning pipeline orchestration via Dataiku (DSS 10+) platformHands on experience working in Agile teams, processes, and practicesPharma & Life Science commercial functional knowledgePharma & Life Science commercial data literacyExperience with Dataiku Data Science StudioAbility to work non-traditional work hours interacting with global teams spanning across the different regions (eg: North America, Europe, Asia)Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.Information & Business Tech#LI-PFE

Pfizer