Milliman – Trainee Engineer
Job Description
Milliman is among the world’s largest providers of actuarial and related products and services. Our mission is to serve our clients to protect the health and financial well-being of people everywhere. Founded in 1947, Milliman is an independent firm with offices in major cities around the globe. We are owned and managed by our principals—senior consultants whose selection is based on their technical, professional, and business achievements. Milliman serves the full spectrum of business, financial, government, union, education, and nonprofit organizations. In addition to our consulting actuaries, Milliman’s body of professionals includes numerous other specialists, ranging from clinicians to economists.
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Responsibilities
AI & Machine Learning Model Development:
Design, develop, and optimize AI/ML models for tasks such as anomaly detection, fraud detection, and predictive analytics using healthcare claims data.
Implement and fine-tune Generative AI and agentic AI algorithms for data synthesis and decision-making.
Collaboration & Implementation:
Collaborate with cross-functional teams, including data engineers, software developers, and healthcare domain experts, to integrate AI solutions into existing workflows.
Deploy machine learning models into production environments and monitor their performance over time.
Research & Innovation:
Stay updated with advancements in AI/ML technologies and propose innovative approaches for solving complex healthcare challenges.
Experiment with state-of-the-art frameworks and techniques to improve model performance and scalability.
Documentation & Compliance:
Communicate complex technical concepts to both technical and non-technical audiences.
Ensure all models and workflows comply with relevant data privacy and security standards (e.g., HIPAA).
Document processes, results, and best practices for knowledge sharing and reproducibility.
Educational Background:
Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, Artificial Intelligence, or a related field.
Certifications in Azure, Databricks, or relevant AI/ML technologies are a plus.
Professional Experience:
Hands-on experience through academic projects, internships, or personal work in building machine learning models (e.g., classification, regression, clustering, time series, or NLP).
Familiarity or coursework exposure to cloud platforms and data/ML tooling (Azure, Databricks, or similar) for data processing and model experimentation.
Technical Expertise:
Strong proficiency in Python, SQL, and relevant ML libraries/frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Expertise in data manipulation and analysis using Pandas, NumPy, and PySpark and cloud platforms (Azure, AWS, GCP), with a focus on Azure Databricks.
Experience in building and fine-tuning anomaly detection algorithms and predictive models. Experience with data visualization tools (Power BI, Tableau, Matplotlib, Seaborn).
Domain Knowledge:
Familiarity with healthcare claims data structures, terminologies (e.g., ICD codes, CPT codes), and workflows.
Understanding of healthcare compliance and data privacy standards (e.g., HIPAA).
