Staff Software Engineer/Data Scientist

Responsibilities

• Design & Deliver AI Solutions: Build statistical, ML, and generative/agentic AI solutions spanning RAG pipelines, chat/assistants, classification, forecasting, and recommendation systems using a fit-for-purpose toolkit from traditional predictive modeling to agentic workflows.
• Knowledge Base Engineering for Strategic Domains: Engineer and maintain domain-specific knowledge bases (regulatory intelligence, competitive insights, customer sentiment) to power generative applications across underwriting, pricing, and service.
• Prompt & Agent Design: Author robust system prompts, few-shot patterns, and structured outputs (e.g., JSON schemas). Define safe tool-use policies and function/structured calling for reliable agent behavior.
• Synthetic Data Generation & Augmentation: Develop and validate synthetic data pipelines to alleviate sparsity and accelerate convergence, especially for low-frequency perils and emerging segments, while preserving privacy and distributional fidelity.
• Customer Experience Optimization: Apply GenAI to elevate self-service, virtual assistants, and inspection automation, driving personalization, speed, and operational efficiency.
• Architectural Collaboration & MLOps Integration: Partner with enterprise architects and platform teams to ensure scalable, secure deployments via unified systems. Standardize experiment tracking, registries, evaluation gates, and CI/CD patterns across clouds and services.
• Innovation & Continuous Learning: Identify and pilot emerging methods (OCR, rerankers, PEFT/LoRA, distillation). Build reusable accelerators (chunking templates, prompt registries, evaluation harnesses). Stay current on AI/ML, LLMOps, NLP, RAG, and responsible AI.
• Required Skills & Experience :
• Experience Range - 6 to 9 Years
• Experience in statistical modeling and machine learning using Python, including extensive use of pandas, NumPy, scikit-learn, and strong SQL for data exploration, feature development, and knowledge preparation; familiarity with PyTorch and/or TensorFlow preferred.
• Experience across the end-to-end modeling lifecycle, including problem framing and requirements gathering, experiment design, offline evaluation, and ongoing production validation and monitoring.
• Solid understanding and practical application of core machine learning methods, with 3+ years of experience applying deep learning architectures in real-world use cases.
• Experience designing and operationalizing model evaluation and monitoring approaches, including test set creation (gold and/or synthetic), metric definition and tracking (e.g., classification, forecasting, ranking/IR, and business KPIs), and supporting A/B testing, drift detecti

Additional Information

IND Staff Engineer - GCC094 We're determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals - and to help others accomplish theirs, too. Join our team as we help shape the future.

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