About Milliman
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.

About Milliman’s MedInsight Team
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 skilled Machine Learning and AI Engineer to join our innovation Healthcare IT team. This role focuses on developing cutting-edge AI models, including Generative AI and agentic AI systems, to analyze healthcare complex claims datasets to extract valuable insights and create further analytics such as models for anomaly detection and predictive insights. The ideal candidate will have experience in machine learning, data analysis, and cloud-based platforms, specifically Databricks and Azure, and will play a key role in shaping the future of AI-driven healthcare solutions.

Key Responsibilities:

• 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.
• Work with large-scale structured and unstructured healthcare claims data to preprocess, clean, and transform datasets for ML pipelines using Pyspark and other relevant tools.
• Perform exploratory data analysis to understand data characteristics and identify potential insights Engineer relevant features to improve model performance.
• Develop and fine-tune machine learning models using techniques like regression, classification, clustering, and time series analysis.
• Employ advanced techniques like deep learning and natural language processing when appropriate.
• Build and maintain scalable data pipelines on Databricks and Azure.
  
• 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.
• 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.
• 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:

• 2-5 years of hands-on experience in designing and implementing machine learning models, preferably in the healthcare or insurance domain.
• Experience working with cloud-based platforms like Azure and Databricks for data processing and model deployment.
• 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).
• 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 systems.
• Knowledge of healthcare fraud detection systems or predictive analytics in insurance.
• Familiarity with MLOps practices, including model versioning, monitoring, and CI/CD pipelines.