Sr Applied AI Data Scientist

The Small Business Applied AI team sits at the intersection of advanced analytics, AI innovation, and business strategy - delivering scalable, high impact solutions that enhance underwriting, product, and distribution in a dynamic competitive environment.  We design and deploy cutting edge AI and ML solutions that drive smarter decisions, operational efficiency, and competitive advantage at scale.

Core Technical Capabilities 

• Develop AI solutions: Create ML and generative AI systems for RAG pipelines, chatbots, classification, forecasting, and recommendation. Ensure alignment with enterprise standards, seamless integration, and secure scalability. 

• End‑to‑End Solution Development: Own the AI lifecycle from problem framing through deployment: data prep, modeling, evaluation, model change management, orchestration, observability, drift detection, and synthetic data generation. 

• Collaborate closely with AI engineers, data engineers, platform, security, and IT to ensure solutions are robust, maintainable, production ready, follow safety filters/guardrails, and rollback plans. 

• Prompt and AI Agent design: Experience with system prompts, one-shot, few-shot, and ability to automate or optimize prompts using LLM as a prompt enhancer.  Ability to design and orchestrate multi-agent pipelines such as sequential with well-orchestrated callbacks for deterministic AI agent, tool, or LLM behavior. 

• Project management for AI delivery: Drive execution from discovery to rollout by defining scope, milestones, and acceptance criteria; managing dependencies/risks; coordinating cross-functional workstreams; and maintaining clear status reporting, issue escalation, and delivery timelines. 

Stakeholder Collaboration, Domain Knowledge & Influence 

• Partners closely with Product, Underwriting, Distribution, Risk, Legal, and Compliance to align AI initiatives with enterprise objectives and governance expectations. 

• Translates complex model behavior and evaluation outcomes into clear, actionable business insights with defined success criteria (accuracy, cost, performance, reuse, ROI). 

• Develops deep understanding of insurance business processes, data, and regulatory constraints, embedding domain taxonomies, access controls, and security into solution design. 

Required Skills 

• Proficiency in Python and SQL (ideally Snowflake); experience with pandas, numpy, scikit-learn. 

• Strong foundation in ML, deep learning, NLP; familiarity with PyTorch/TensorFlow and generative AI. 

• Experience with cloud tools (Google Vertex AI, AWS SageMaker/Bedrock). 

• Ability to build reproducible workflows using Jupyter and GIT. 

• Competence in end-to-end modeling: requirements, experiment design, evaluation, production monitoring. 

• Experience tracking forecasting metrics (MAPE/WAPE) and LLM evaluation. 

• Understanding agentic AI pipelines and prompt engineering for language models. 

• Ability to frame business problems with hypothesis-driven approaches and experimentation. 

• Excellent communicator—able to translate analytics into clear business narratives for stakeholders. 

Good to Have Skills 

• Object oriented programming and Google ADK. 

• Knowledge of embeddings and similarity (e.g., cosine-similarity). 

• Google Cloud Platform literacy. 

• Advanced PyTorch/TensorFlow skills. 

• NLP & Generative AI: hybrid+dense retrieval, chunking, structured outputs. 

• Customer sentiment modeling from diverse sources. 

• Innovation & continuous learning—driving enterprise capabilities with emerging AI techniques. 

Education & Experience 

• 6+ years with Bachelor’s degree; less for Master’s/Ph.D. 

• Master’s or Ph.D. preferred in Machine Learning, Applied Math, Data Science, Computer Science, or related fields. 

Candidate must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.