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 Digitals 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 Pfizers 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 Pfizers 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

• Bachelors 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)