Data Science Industrialization

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 an ML Engineer, you will be part of 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 member 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

• Convert data/ML pipelines into scalable pipelines based on the infrastructure available (e.g. convert Python based data science code into PySpark/SQL for scalable pushdown execution)
• Enable production models across the ML lifecycle
• Implement model performance metrics and model monitoring dashboards
• Implement model retraining trigger mechanisms
• Implement champion/challenger model and A/B testing automation
• Implement CI/CD orchestration for data science pipelines
• Manage the production deployments and post-deployment model lifecycle management activities: drift monitoring, model retraining, and model technical evaluation & business validation
• Work with stakeholders to assist with ML pipeline -related technical issues and support modeling infrastructure needs

Qualifications

Must-Have

• Bachelor’s degree in ML engineering related area (Data Science, Computer Engineering, Computer Science, Information Systems, Engineering or a related discipline)
• 2+ years of work experience in data science, analytics, or engineering for a diverse range of projects
• Understanding of data science development lifecycle (CRISP)
• Hands-on skills in ML engineering and data science (e.g., Python, R, SQL, industrialized ETL software)
• 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 with data science enabling technology, such as Dataiku Data Science Studio, AWS SageMaker or other data science platforms
• Hands on experience working in Agile teams, processes, and practices
• Understanding of MLOps principles and tech stack (e.g. MLFlow)
• Experience in CI/CD integration (e.g. GitHub, GitHub Actions or Jenkins)
• Experience working in a cloud based analytics ecosystem (AWS, Snowflake, etc)

Work Location Assignment: Flexible

Information & Business Tech

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