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 the ML Engineering Lead, you will be a leader within the Data Science Industrialization team charged with building and automating high quality data science pipelines that power key business applications with advanced analytics/AI/ML.  You will be a leader of a global team that defines and maintains ML Ops best practices and deploys and maintains production analytics and data science modeling workflows.

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, ML Ops, and engineering
• Interface with Enterprise Architecture team to operationalize the vision of MLOps enablement
• Build ML engineering capabilities and contribute to the broader talent building framework
• Provide direction for ML engineering research, design, and implementation of best practices, and facilitate related trainings
• 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
• Lead the advancement of at scale MLOps enablement across bespoke analytics
• Lead ML engineering deployments to enable production models across the ML lifecycle including model training, monitoring, and retraining where required
• Lead ML engineering deployments to enable production models across the ML lifecycle including model training, monitoring, and retraining where required
• Lead implementation of CI/CD orchestration for data science pipelines
• Partner with AIDA Data team to integrate developed ML 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

Qualifications

Must-Have

• Bachelor’s degree in ML engineering related area (Data Science, Computer Engineering, Computer Science, Information Systems, Engineering or a related discipline)
• 7+ years of work experience in data science, analytics, or engineering for a diverse range of projects
• Deep expertise with data science enabling technology, such as Dataiku Data Science Studio, AWS SageMaker or other data science platforms
• Strong understanding of data science development lifecycle (CRISP)
• Strong hands-on skills in ML engineering and data science (e.g., Python, industrialized ETL software)
• Deep understanding of MLOps principles and tech stack (e.g. MLFlow)
• Experience working in a cloud based analytics ecosystem (AWS, Snowflake, etc)
• Experience in CI/CD integration (e.g. GitHub, GitHub 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 ML engineering teams
• Hands on experience working in Agile teams, processes, and practices
• Experience in solution architecture & design
• Experience in software/product engineering
• Strong hands-on skills for data and machine learning pipeline orchestration via Dataiku (DSS 9 or 10) platform
• 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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