Machine Learning Engineer - Express Scripts

Job Description

Job Title:

Machine Learning Engineer
Location: Mississauga
Employment Type: Full-time
Reason for Vacancy: Replacement
Work Arrangement: Hybrid
Department Name: Application Delivery Department
Pay Typed: Salaried
Pay Range: $115,000 - $125,000 annually

Please note that this is a general posting range and offer range will be varied based on relevant experience, qualifications, skills required and location for this role.

About UsExpress Scripts Canada (ESC) is the leader in health benefits management. Serving over 12 million members, we help insurance carriers, third party administrators, and the public sector optimize the value of health benefits by linking the talent and professional expertise of our people with leading edgeinformation management systems and technology. Express Scripts Canada is a wholly owned subsidiary of Express Scripts, one of the largest pharmacy benefit management (PBM) companies in North America, part of the company (NYSE: CI), a global health company.

Together, we deliver innovative,cost-effective solutions that improve access, affordability, and health outcomes for Canadians.

Job Summary Express Scripts Canada is looking for a Machine Learning Engineer to join our Team. The successful candidate will design, build, and operate data pipelines and ML services that detect unusual patterns in healthcare claims and pharmacies.

The role blends hands-on data engineering (Oracle ETL), model development (unsupervised learning), and API serving (Python/FastAPI), with close collaboration across business teams. You’ll ensure the best possible performance, quality, and responsiveness of the applications and pipelines, and help maintain code quality, organization, and automation.

• Design implement ML data pipelines to ingest, engineer, and persist features from Oracle (SQL Alchemy/oracledb), including robust logging, argument-driven CLI tools, and environment-based configuration (.env).
• Build and validate unsupervised ML models (e.g., MiniBatchKMeans/KMeans, DBSCAN) with dimensionality reduction (TruncatedSVD/PCA), leveraging chunked processing and sparse matrices for large datasets; evaluate using silhouette/Calinski-Harabasz/Davies-Bouldin and stability checks.
• Serve models as REST APIs (ie. FastAPI/Pydantic) with health endpoints, CORS, structured response models, and joblib artifact loading; instrument application logs and operational run scripts.
• Orchestrate end-to-end validation (DB → feature engineering → API scoring → curated outputs), writing curated results back to Oracle tables and creating/maintaining schemas and DDL where required.
• Collaborate with business teams to interpret clusters/risk buckets, explain top contributing features, and incorporate feedback loops into subsequent runs and outputs.
• Support deployment workflows in sandboxed/on-prem environments; participate in CI/CD (e.g., Jenkins/Open Shift pipelines) as part of model operationalization.
• Ensure performance, quality, and responsiveness of data pipelines and APIs; help maintain code quality, organization, and automation; perform code reviews and follow Agile ceremonies.
• Understand how AI is interpreting the data set and use that understanding to build prompt that lead to expected outcomes
• Develop and maintain AI pipelines including data preprocessing, feature extractions, model training, and evaluation

What We’re Looking For

• 5 years application development experience;3–5 years of professional experience in data science / machine learning engineering with Python, including product ionizing data pipelines or ML services
• Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field (or equivalent practical experience).
• Core programming (Python):
  Strong with Num Py, pandas, scikit-learn, Sci Py (sparse), joblib, CLI (argparse), and data visualization (matplotlib/seaborn).
• Modeling (Unsupervised):
  Practical experience with MiniBatchKMeans/KMeans, DBSCAN, TruncatedSVD/PCA, cluster evaluation (silhouette, CH, DB), and stability/bootstrapping.
• Data engineering and Oracle:
  Writing performant SQL; using SQL Alchemy and oracledb for reads/writes; creating tables/DDL;…