Milliman IntelliScript is looking to enhance its existing data-driven, software-as-a-service (SaaS) products and develop new ones in response to client needs and opportunities within the insurance, health IT, and life sciences sectors. This involves leveraging advanced data science, machine learning, and GenAI/NLP expertise to provide sophisticated analytical solutions for healthcare-related problems, ultimately helping clients make effective underwriting and pricing decisions.
Requirements
- 7+ years of professional experience using AI/ML to create high return on investment commercial data science solutions
- Expert data scientist with demonstrated expertise in at least one core area of AI/ML and strong working knowledge across others, including supervised and unsupervised learning, model validation, deep learning architectures, and NLP algorithms.
- Supervised Learning: Linear/logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM, CatBoost), and ensemble methods
- Unsupervised Learning: K-means clustering, hierarchical clustering, PCA, and anomaly detection algorithms
- Model Validation: Cross-validation strategies, hyperparameter optimization (Grid Search, Random Search, Bayesian optimization), and A/B testing frameworks
- Deep Learning Architectures: Neural networks, transformers, and transfer learning methodologies
- NLP Algorithms: Text preprocessing, TF-IDF, word embeddings (Word2Vec, GloVe), topic modeling (LDA), sentiment analysis, and named entity recognition
Responsibilities
- Research, develop, deploy, and maintain traditional AI/ML models following industry best practices
- Work extensively with available GenAI models; construct exciting solutions to internal and external use cases across markets and enhance our internal capabilities through centralized internal tooling and thought leadership
- Coordinate with Product, Business Development, ML Engineering, and IT to bring new and exciting data science products to market, as well as support existing industry leading products
- Help drive best practices and continuous improvement on the data science team; influencing model design and experimentation strategy through planning, audit, peer review, and other coaching
Other
- Excellent communication skills, in person and through phone / email
- Proven ability to understand client analytical needs, translate them into an action plan, then execute and deliver
- Customer-centric approach to finding solutions
- Focused on results and able to explain findings in a way that answers business problems
- Constructive, "can do" approach to overcoming obstacles
