## Overview Bold Penguin is the premier digital broker for commercial insurance. Their technology expands top-of-funnel demand and increases yield for enterprise partners. Through digital distribution, wholesale capabilities, and AI-driven insights, they transform the lifecycle of both simple and complex risks, from prospecting to placement. Benefits We offer competitive compensation and progressive benefits that include: - Medical, Dental, and Vision - Flexible PTO Policy - 401(k) with a company match - Employee Assistance Program - Parental Leave - Disability and Life Benefits​ Stay connected to the Glacier. We have great SLACK channels for work and play. We also like to video conference and hold all-hands "Waddles" regularly. Penguin bling. Like swag themed after a certain Antarctic bird? Just. You. Wait. Bold Penguin believes in inclusion. That’s why we’re proud to be an equal opportunity employer that considers all qualified applicants regardless of race, color, religion, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. To learn more about our results-focused culture and employee-focused perks, read more on our [careers page](https://www.boldpenguin.com/careers). ## Description YOUR ROLE You will lead the data science projects and mentor the junior team members. You need to have extensive knowledge of advanced Large Language Models (LLMs), including Retrieval-Augmented Generation (RAG) and fine-tuning techniques. This role requires a hands-on leader who is eager to drive projects to completion, support peers and junior data scientists, and resolve production issues promptly whenever needed. WHAT YOU'LL DO - Project Ownership: Take full ownership of data science projects from inception to deployment. Ensure timely and successful project delivery by overseeing the entire data science pipeline. - Mentorship: Mentor and guide junior data scientists, fostering a collaborative and learning environment. Provide technical and professional development support to team members. - Agentic AI Systems: Design, develop, and deploy agentic AI solutions capable of reasoning, planning, tool usage, and autonomous task execution. Evaluate and implement emerging agent frameworks and orchestration patterns. Build multi-agent workflows to automate complex business processes and improve operational efficiency. - Advanced LLM Knowledge: Apply advanced knowledge of Large Language Models (LLMs), including Retrieval-Augmented Generation (RAG) and fine-tuning. Stay updated with the latest advancements in LLM technologies and implement them in projects. - Proactivity and Execution: Demonstrate a proactive attitude and eagerness to get things done efficiently. Drive project initiatives and ensure continuous progress. - Peer and Junior Work Review: Review the work of peers and junior data scientists, providing constructive feedback and support. Ensure high-quality deliverables through thorough evaluation and guidance. - Production Issue Resolution: Take responsibility for fixing data science-related production issues promptly. Implement robust solutions to minimize downtime and ensure smooth operations. - AWS SageMaker, Bedrock, and AgentCore Expertise: Utilize AWS SageMaker for building, training, fine-tuning, and deploying machine learning and generative AI models. Leverage AWS Bedrock and Bedrock AgentCore to develop, deploy, and manage production-grade AI agents, Retrieval-Augmented Generation (RAG) solutions, multi-agent workflows, and tool-calling applications. Integrate foundation models with enterprise systems, APIs, vector databases, and knowledge bases while ensuring scalability, security, and operational excellence. - Other duties and responsibilities as assigned. ## Requirements YOUR LOCATION This is a fully remote role, with the exception of onboarding and optional in-office events. S kills & Experience - Bachelor's degree, Master’s degree, or PhD in Computer Science, Data Science, Statistics, or a related field. - 5+ years proven experience as a Senior Data Scientist, with expertise in advanced LLMs like RAG and fine-tuning. - Hands-on experience building agentic AI applications and multi-agent systems. - Strong understanding of MCP (Model Context Protocol), tool calling, and AI agent orchestration patterns. - Expertise in Retrieval-Augmented Generation (RAG), vector databases, semantic search, and knowledge retrieval systems. - Solid understanding of machine learning algorithms and statistical analysis. - Familiarity with agent evaluation frameworks, observability, and production monitoring. - Knowledge of prompt engineering, agent planning, memory management, and context optimization techniques. - Excellent problem-solving skills and attention to detail. - Strong leadership, communication, and collaboration skills. - Ability to mentor and guide junior team members effectively. - Proactive and results-driven mindset. Physical Requirements - Must be able to sit/stand/walk for prolonged periods of time, (up to 8 hours per day) at a desk working on a computer. - Must be able to use standard office equipment for extended periods of time, including but not limited to, a mouse, keyboard, phone and video conferencing.