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 Data Science Solutions and Initiatives leads the scaling of data and insights capabilities - critical drivers and enablers of Pfizer\xe2\x80\x99s digital transformation.

As a Manager, Data Science Operations Delivery Oversight, you will be part of the Data Science Industrialization team charged with productionizing high quality data science pipelines that power key business applications with advanced analytics/AI/ML. You will be a member of a global team that defines and maintains ML Ops best practices and deploys and maintains production analytics and data science modeling workflows. Your operational focus will be on productionizing data science workflows, including automating data ingestion and processing, analytic modeling and measurement processes, AI/ML models, and production workflow deployments.

Role Responsibilities

• Oversee conversion of data science workflows into scalable pipelines follows best practices laid out by the workflow productionization team and platform team
• Automate analytic workflows and processes for delivering data science analytic outputs (models, scored datasets, predicted outputs)
• Design and oversee implementation of data and model tracking and monitoring dashboards
• Ensure quality of deliverables and adherence to best practices of data science workflow productionization processes
• Oversee operations supporting productionization of analytic outputs in data science workflows and tools
• Lead design and build of backend operations to support data science and analytic accelerators
• Implement CI/CD orchestration for data science and analytics workflows
• Oversee production deployments and post-deployment model lifecycle management activities: drift monitoring, model retraining
• Support data science workflow-related technical issues and modeling infrastructure needs

Qualifications

Must-Have

• Bachelor\xe2\x80\x99s degree in data science, analytics, or engineering related area (Data Science, Computer Engineering, Computer Science, Information Systems, Engineering or a related discipline)
• 5+ years of work experience in data science, analytics, or engineering, with a preference for experience in the Commercial pharmaceutical analytics space
• Pharma & Life Science commercial functional knowledge
• Pharma & Life Science commercial data literacy
• Understanding of data science development lifecycle (CRISP)
• Understanding of MLOps principles and tech stack (e.g. MLFlow)
• Strong hands-on skills in ML engineering and data science (e.g., Python, R, SQL, industrialized ETL software)
• 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, Analytics, Computer Engineering, Computer Science, Information Systems or related discipline
• Hands on experience working in Agile teams, processes, and practices
• Experience leading or providing oversight for offshore analytics teams
• Experience with data science enabling technology, such as Dataiku Data Science Studio, AWS SageMaker or other data science platforms
• Experience in CI/CD integration (e.g. GitHub, GitHub Actions or Jenkins)
• Experience in software/product engineering
• Hands-on skills for data and machine learning pipeline orchestration via Dataiku platform
• Experience with Dataiku Data Science Studio

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.

Information & Business Tech

#LI-PFE

Pfizer