CDNA_ML Engineer - Data Science Enablement

usage, and dataset preparation.

• Help prototype and deploy internal tools (e.g., Streamlit apps) to visualize model results or enable user interactions.

• Follow engineering best practices around version control, documentation, and testing in shared repositories.

• Collaborate with enterprise engineering and data platform teams to ensure compliance with cloud architecture, data privacy, and governance standards.

Thrive | What you can expect

As we work to develop treatments that take care of others, we also work to care for our teammates' professional and personal growth and well-being.

Basic Qualifications

• Bachelor's or Master's in Computer Science, Data Engineering, or related technical field.

• 3-5 years of experience in ML engineering, data engineering, or DevOps supporting ML pipelines.

• Proficiency in Python and SQL, with working knowledge of Databricks, MLflow, and AWS (S3, EC2, Lambda).

• Experience supporting model deployment, monitoring, and packaging workflows.

• Ability to document work clearly and collaborate effectively with cross-functional teams.

Preferred Qualifications

• Familiarity with vector stores, embedding pipelines, or RAG-style use cases.

• Experience developing or supporting Streamlit or Dash-based apps for data products.

• Working knowledge of Git-based workflows, containerization, and CI/CD pipelines.

Passion for enabling data science teams and driving engineering quality.

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