Job Description

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\xe2\x80\x99s 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 Pfizer\xe2\x80\x99s transformation into a digitally driven organization that leverages data science and advanced analytics to change patients\xe2\x80\x99 lives. The Data Science Industrialization team within the Artificial Intelligence COE leads the scaling of data and insights capabilities - critical drivers and enablers of Pfizer\xe2\x80\x99s digital transformation.

As the Data Science Workflow Productionization Lead, you will be a leader within the Data Science Industrialization team charged with engineering automated production-grade data science pipelines that power key business applications with advanced analytics/AI/ML. As a thought leader in driving CI/CD orchestration of data and AI/ML operations to enable industrialized data science, you will lead a global team and partner with cross-functional business stakeholders and other AI leaders. You will catalyze identification, design, iterative development, and continuous improvements of monitoring and deployment processes to support production data science workflows. Your team will develop best practices and maintain standards for data ops, AI/ML ops, and production workflow deployments to enable automation, embed AI/ML into decision intelligence applications, and enable self-service insight generation. In addition, you will be responsible for providing critical input into the analytics 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, and Finance) for engineering automated production-grade data, analytics, and data science modeling workflows that support major business objectives across all of Pfizer\xe2\x80\x99s core business units.

Role Responsibilities

• Lead implementation of data engineering and AI/ML engineering, and production deployments of data science workflow products with automated self-monitoring QA/QC processes, industrialized workflow accelerators, and best practices in the engineering of production-grade AI/ML analytic insights products
• Lead development of and provide oversight for managed service lines related data engineering & ops, AI/ML engineering & ops, and processes for production deployments of data science projects and workflows
• Act as a subject matter expert for data engineering, AI/ML engineering, and production deployment processes of data science workflows on cross functional teams in bespoke organizational initiatives by providing thought leadership and execution support for engineering and production deployment needs to embed AI/ML into decision intelligence applications and enable self-service insight generation
• Direct data science engineering and operations 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 trainings
• Set a vision, prioritize workstreams, and provide day-to-day leadership, supervision, and mentorship for a global team with technical & functional expertise that includes analytics, data science, data engineering, AI/ML engineering, and operations
• Coach direct reports through technical and organizational thought leadership and innovation
• Communicate value delivered through data engineering, AI/ML engineering, and production-grade data science workflows to end user functions (e.g., Chief Marketing Office, Pfizer Biopharma Commercial and Medical Affairs) and evangelize innovative ideas on data and AI/ML engineering and ops to enable self-service and always-on insight generation
• Partner 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 development
• Partner with AIDA Platforms team on end to end capability integration between enterprise platforms and internally developed accelerators (API registry, ML library / workflow management, enterprise connectors)
• Partner with AIDA Platforms team to define best practices for self-serve production deployment of data science workflows to reduce effort and turn-around times

Qualifications

Must-Have

• Bachelor\xe2\x80\x99s degree in data science, engineering, or operations related area (Data Science, 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 projects
• 2-3 years of hands-on experience leading data science or AI/ML engineering teams
• Track record of managing stakeholder groups and effecting change
• Recognized by peers as an expert in data science, AI/ML engineering, and AI/ML ops with deep expertise in CI/CD for monitoring and orchestration of data science workflows, and hands-on development within data science enabling technologies such as Dataiku Data Science Studio, Domino Data Science Platform, Cloudera Data Science Workbench, AWS Sagemaker, Databricks, or other data science platforms
• Understands 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 stakeholders
• Clearly articulates expectations, capabilities, and action plans; actively listens with others\xe2\x80\x99 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; 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 of the 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
• Ability to manage projects from end-to-end, from requirements gathering through implementation, hypercare, and development of support processes to ensure longevity of solutions
• Demonstrated experience interfacing with internal and external teams to develop innovative data science solutions
• Strong 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 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
• Experience in solution architecture & design
• Experience in software/product engineering
• Strong hands-on skills for data and machine learning pipeline orchestration via Dataiku (DSS 10+) platform
• Hands on experience working in Agile teams, processes, and practices
• Pharma & Life Science commercial functional knowledge
• Pharma & Life Science commercial data literacy

Work Location Assignment: Flexible

Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.

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