ML Engineer - Data Science Industrialization

About the role

Pfizer's Digital Artificial Intelligence, Data and Analytics (AIDA) organization is looking for an ML Engineer to join the Data Science Industrialization team within Enterprise Data Science and Advanced Analytics. The team leads the scaling of data and insights capabilities that drive Pfizer's digital transformation. You will build and automate high-quality data science pipelines that power key business applications with advanced analytics, AI and ML, and help define and maintain ML Ops best practices.

Key responsibilities

• Convert data and ML pipelines into scalable ones based on available infrastructure, for example turning 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 production deployments and post-deployment model lifecycle management, including drift monitoring, model retraining and model technical evaluation and business validation.
• Work with stakeholders to resolve ML pipeline technical issues and support modeling infrastructure needs.

Required profile

• Bachelor's degree in a field related to ML engineering (Data Science, Computer Engineering, Computer Science, Information Systems, Engineering or a related discipline).
• 2+ years of work experience in data science, analytics or engineering on a diverse range of projects.
• Understanding of the data science development lifecycle (CRISP).
• Hands-on ML engineering and data science skills (e.g. Python, R, SQL, industrialized ETL software).
• Strong English communication skills (written and verbal).
• Nice to have: advanced degree, experience with platforms such as Dataiku Data Science Studio or AWS SageMaker, Agile experience, understanding of MLOps (e.g. MLFlow), CI/CD tools (GitHub, GitHub Actions, Jenkins) and cloud analytics ecosystems (AWS, Snowflake).