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 patient’s 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 the Analytics Engineering Lead, you will be a leader within the Data Science Industrialization team charged with building and maintaining industrialized analytic data and code assets that power key business applications.  You will be a leader of a global team developing data pipelines, optimizing feature creation, and managing and optimizing performance of data and code assets. 

Role Responsibilities

• Set a vision and provide day-to-day leadership, supervision, and mentorship for a team of individual contributors with functional expertise that includes analytics, data science, and engineering
• Build analytics engineering capabilities and contribute to the broader talent building framework
• Provide direction for analytics engineering research, design, and implementation of best practices, and facilitate related trainings
• Provide strategic and technical input for data science industrialization roadmap
• Establish and promote technical delivery practices and technical documentation standards
• Provide input on platform evolution, vendor scan, and overall data science industrialization capability roadmap development
• Lead the advancement of at scale “industrialized” data science capabilities and analytic products
• Lead development of parametrized workflows, data assets, and reusable widgets in the delivery of AI/ML analytic insights products 
• Ensure industrialized components fully enable interoperable data preparation and modeling workflows within the end-to-end analytics ecosystem
• Partner with AIDA Data team to integrate developed data pipelines into enterprise-level analytics data products where appropriate
• Partner with AIDA Platforms team on continuous development and end to end capability integration between OOB platforms and internal engineered components (API registry, ML library / workflow management, enterprise connectors); Performance and resource optimization of managed pipelines and models
• Lead implementation of CI/CD orchestration for data science pipelines
• Lead design and development of automated and self-monitoring data pipelines including automated QA/QC processes

Qualifications

Must-Have

• Bachelor’s degree in analytics engineering related area (Data Science, Computer Engineering, Computer Science, Information Systems, Engineering or a related discipline)
• 7+ years of work experience in data, analytics, or engineering for a diverse range of projects
• Deep expertise with data science enabling technology, such as Data Science Studio 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 in a cloud based analytics ecosystem (AWS, Snowflake, etc)
• Experience working with various types of data (structured / unstructured)
• Experience in data ingestion, data warehousing, and data model concepts
• Experience with relational and dimensional data structures, theories, and practices
• Experience in CI/CD integration (e.g. Git Hub, Git Hub Actions or Jenkins)
• 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 experience leading data science or analytics engineering teams
• Strong hands-on skills in Javascript libraries/frameworks for visualization (D3, React, Angular)
• Hands on experience working in Agile teams, processes, and practices
• Experience in software/product engineering
• Understanding of data science development lifecycle (CRISP)
• Strong hands-on skills in containerization (e.g. AWS EKS, Kubernetes)
• Strong hands-on skills for data pipeline orchestration (e.g. Airflow)
• 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

LI#PFE