The Milliman MedInsight practice has assisted many healthcare organizations in evaluating and developing solutions to complex business problems. Our consultants are experienced in the key issues related to healthcare operations and the use of technology to support those operations. Because of our focus on those unique technology and operations issues facing the healthcare industry, we are uniquely qualified to assist organizations in solving complex business problems. Our Health IT software team has been developing and selling data warehousing solutions for over twelve years.
Job Summary
We are seeking a passionate and motivated Trainee AI Engineer to join our innovative Healthcare IT team. This internship focuses on supporting the development of AI/ML solutions-including Generative AI and agentic AI approaches-to analyze complex healthcare claims datasets, extract insights, and contribute to analytics such as anomaly detection and predictive modeling. The ideal candidate has a strong foundation in machine learning and data analysis, is comfortable working in cloud-based environments (especially Databricks and Azure), and is eager to learn and collaborate in building AI-driven healthcare solutions.
Key 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.
Required Qualifications:
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).
Soft Skills:
- Strong analytical and problem-solving skills with attention to detail.
- Excellent communication and collaboration skills for working in cross-functional teams.
- Ability to manage multiple priorities in a fast-paced environment.
- Ability to work independently and collaboratively in a fast-paced environment.
- Passion for data and a drive to uncover insights.
Preferred Qualifications:
- Experience with Generative AI frameworks (e.g., GPT models, VAEs) and agentic AI
- Knowledge of healthcare fraud detection systems or predictive analytics in insurance.
- Familiarity with MLOps practices, including model versioning, monitoring, and CI/CD pipelines
