• Experience in statistical modeling, machine learning, and advanced analytics using Python, including pandas, NumPy, scikit-learn, and strong SQL for complex data exploration, feature engineering, and knowledge preparation; familiarity with PyTorch and/or TensorFlow preferred.
• Experience across the end-to-end modeling and analytics lifecycle, including requirements gathering, experiment design, offline evaluation, and basic production monitoring and validation of AI and BI solutions.
• Solid understanding and practical application of core machine learning, deep learning, and natural language processing algorithms and architectures.
• Experience designing and implementing business intelligence and semantic layer solutions, including dimensional modeling, fact tables, metrics definition, and operating within data warehouse or data lake environments.
• Experience designing and operationalizing evaluation and monitoring strategies, including test set creation (gold and/or synthetic), defining and tracking metrics such as classification, forecasting, ranking/IR, RAG faithfulness and truthfulness, and customer or operational KPIs, with support for A/B testing and drift detection.
• Experience designing, developing, and deploying conversational AI solutions, including chatbots, virtual assistants, intelligent agents, and Retrieval-Augmented Generation (RAG) pipelines.
• Experience working with unstructured data, including document parsing and OCR fundamentals, robust text normalization, metadata and lineage awareness, and PII detection or redaction considerations for insurance documents.
• Experience working with cloud-based AI and analytics platforms such as Google Vertex AI, AWS SageMaker or Bedrock, or Azure AI Services, supporting experimentation, deployment, and conversational AI use cases.
• Experience using Git and Unix-based environments, building reproducible notebooks or pipelines, and applying basic container and cloud concepts to support reliable analytics and AI workflows.
• Experience communicating complex technical designs, trade-offs, evaluation results, and risks to both highly technical and non-technical business audiences, translating insights into clear business outcomes and strategy.
• Foundational knowledge of insurance products, processes (including underwriting, claims, and pricing), and regulatory environments, with demonstrated ability to apply analytics and AI solutions within a regulated industry context.
• Experience with advanced NLP and Generative AI capabilities, including embeddings, hybrid and dense retrieval strategies, advanced chunking, prompt engineering, structured outputs, and integrating knowledge graphs into RAG solutions for improved grounding.
• Experience or exposure to advanced GenAI applications in insurance, such as compliance-aware prompt engineering, document generation, objection response automation, synthetic data generation for low-frequency events, and customer sentiment modeling from surveys, call transcripts, or inspection notes.
• Experience working within enterprise AI governance frameworks, aligning conversational AI and analytics solutions with compliance, privacy, documentation, and ethical standards.