This position is responsible for ensuring that models are reproducible, compliant, performant, and scalable throughout their lifecycle—from experimentation to production
Job Summary
This position is responsible for ensuring that models are reproducible, compliant, performant, and scalable throughout their lifecycle—from experimentation to production.
The Data Science – MLOps Engineer role collaborates with data scientists, analysts, and commercial operations to design, deploy, and manage machine learning systems that enhance sales effectiveness and engagement.
At AstraZeneca's Alexion division, we champion diversity and foster an energizing culture where new ideas thrive.
Matching Summary
This position is responsible for ensuring that models are reproducible, compliant, performant, and scalable throughout their lifecycle—from experimentation to production.
Skills & Requirements
Must-have
Model Lifecycle Management
Data Pipeline Development
Production Operations
Monitoring & Observability
Model Governance & Compliance
CI/CD tools
Containerization (Docker)
Nice-to-have
Pharmaceutical commercial analytics knowledge
High-throughput inference experience
Incident management skills
Reusable pipelines creation
Excellent communication skills
Teamwork and collaboration
Key Requirements
3-6+ years in ML engineering or MLOps
Bachelor’s or Master’s degree in Computer Science, Data Science, or ML Engineering
SQL, Python, orchestration, data science pipelines
End-to-end ML frameworks experience
MLflow experience
Unit and integration testing experience
Experience with CI/CD tools
Hands-on experience with model packaging and serving frameworks