Design, develop, and deploy AI, Machine Learning, and Generative AI solutions that address business problems across the disability insurance value chain, including claims segmentation, fraud detection, risk scoring, duration modeling, outcome prediction, and next-best-action recommendations.
Translate business, actuarial, and claims objectives into practical AI/ML use cases, analytical designs, and production-ready solution components.
Work across the full ML lifecycle, including problem framing, data exploration, feature engineering, model development, validation, deployment support, monitoring, retraining, and continuous improvement.
Build and optimize supervised, unsupervised, deep learning, NLP, and Generative AI models using structured and unstructured data such as claims history, medical records, claim notes, policy data, and operational data.
Develop NLP and document intelligence pipelines for extracting, classifying, summarizing, and interpreting information from medical records, claim notes, correspondence, and other unstructured documents.
Support Generative AI solution development, including prompt engineering, embeddings, Retrieval-Augmented Generation, vector search, model evaluation, guardrails, and integration with enterprise applications.
Apply robust model validation practices, including performance tuning, bias detection, explainability analysis, stability testing, and business impact assessment.
- Bachelor's or master's degree in Computer Science, Engineering, Data Science, Statistics, Mathematics, Artificial Intelligence, Machine Learning, or a related quantitative discipline.
- 4+ years of hands-on experience in AI/ML, data science, or machine learning engineering, with experience building analytical or AI-enabled solutions that support measurable business outcomes.
- Experience working with structured and unstructured data and applying machine learning, NLP, deep learning, or Generative AI techniques to solve practical business problems.
- Experience supporting model deployment, productionization, monitoring, or MLOps activities will be strongly preferred.
- Exposure to healthcare, insurance, disability claims, financial services, or other regulated industries will be preferred.
- Strong analytical thinking, engineering discipline, and structured problem-solving ability.
- Strong written and verbal communication skills, with the ability to explain analytical findings, model outputs, risks, and recommendations to both technical and business stakeholders.
- Ability to work in a fast-paced, continuously evolving environment and take ownership of challenging AI/ML delivery tasks.
- Ability to collaborate effectively across cultures and work with clients and teams across geographies.
- Strong hands-on proficiency in Python and common AI/ML libraries such as Scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, Hugging Face, LangChain, or similar frameworks.
- Solid understanding of machine learning and statistical modeling techniques, including classification, regression, clustering, survival analysis, anomaly detection, feature engineering, model selection, andhyperparameter tuning.
- Hands-on experience with NLP, deep learning, and document analytics use cases such as text classification, entity extraction, semantic search, summarization, information extraction, and document understanding.
- Practical exposure to Generative AI and Large Language Models, including prompt engineering, embeddings, Retrieval-Augmented Generation, vector databases, LLM evaluation, hallucination control, and responsible AI guardrails.
- Working knowledge of MLOps and production ML practices, including experiment tracking, model registry, CI/CD for ML, model deployment, monitoring, drift detection, retraining, and tools such as MLflow, Airflow,
- Docker, Kubernetes, Git, or cloud-native ML platforms.
- Strong SQL and data manipulation skills, with the ability to work with large structured, semi-structured, and unstructured datasets.
- Good understanding of relational databases, data modeling concepts, data pipelines, APIs, and data architecture fundamentals.
- Exposure to cloud platforms such as Azure, AWS, or GCP and familiarity with scalable AI/ML solution development in cloud environments.
- Ability to evaluate model performance using appropriate metrics, explain model outputs, identify bias or drift, and communicate limitations clearly to technical and non-technical stakeholders.
- Understanding of responsible AI, model governance, data privacy, security, and compliance considerations in regulated domains.
- Exposure to group insurance, healthcare, disability insurance, claims analytics, or other regulated business domains will be preferred.
- Basic understanding of insurance data structures such as policies, insured members, coverages, claims, benefits, providers, medical records, and related entities will be an advantage.
- Strong problem-solving skills, ownership mindset, and ability to design scalable, reusable, and maintainable AI/ML solution components.
- Strong hands-on proficiency in Python and common AI/ML libraries such as Scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, Hugging Face, LangChain, or similar frameworks.
- Solid understanding of machine learning and statistical modeling techniques, including classification, regression, clustering, survival analysis, anomaly detection, feature engineering, model selection, andhyperparameter tuning.
- Hands-on experience with NLP, deep learning, and document analytics use cases such as text classification, entity extraction, semantic search, summarization, information extraction, and document understanding.
- Practical exposure to Generative AI and Large Language Models, including prompt engineering, embeddings, Retrieval-Augmented Generation, vector databases, LLM evaluation, hallucination control, and responsible AI guardrails.
- Working knowledge of MLOps and production ML practices, including experiment tracking, model registry, CI/CD for ML, model deployment, monitoring, drift detection, retraining, and tools such as MLflow, Airflow,
- Docker, Kubernetes, Git, or cloud-native ML platforms.
- Strong SQL and data manipulation skills, with the ability to work with large structured, semi-structured, and unstructured datasets.
- Good understanding of relational databases, data modeling concepts, data pipelines, APIs, and data architecture fundamentals.
- Exposure to cloud platforms such as Azure, AWS, or GCP and familiarity with scalable AI/ML solution development in cloud environments.
- Ability to evaluate model performance using appropriate metrics, explain model outputs, identify bias or drift, and communicate limitations clearly to technical and non-technical stakeholders.
- Understanding of responsible AI, model governance, data privacy, security, and compliance considerations in regulated domains.
- Exposure to group insurance, healthcare, disability insurance, claims analytics, or other regulated business domains will be preferred.
- Basic understanding of insurance data structures such as policies, insured members, coverages, claims, benefits, providers, medical records, and related entities will be an advantage.
- Strong problem-solving skills, ownership mindset, and ability to design scalable, reusable, and maintainable AI/ML solution components.