Blue Rose Research - Product-Focused Data Scientist (LLM Products)
Requirements
• Has 5+ years of experience as a data scientist or applied statistician, with lots of exposure to shipping features or products, not just producing analyses. • Is proficient in Python, SQL, and standard statistical/ML tooling, and comfortable with the mess of real-world data. • Has hands-on experience evaluating or building on top of LLMs: designing eval sets, building LLM-as-judge pipelines, and working with embeddings. • Has good product instincts. Cares deeply about how the end user is going to interact with the model through the product. • Communicates clearly in writing and in conversation with engineers, product managers, and non-technical stakeholders alike. • Is energized by ambiguous problems — translating “help win elections” into a model, a metric, or a product decision, without a clean playbook to follow. • Is motivated by Blue Rose Research’s mission and interested in learning the progressive political ecosystem. Prior political or civic-tech experience is welcome but not required. • We are a remote-first company and we have an office in NYC. • The salary range for this position is $160,000 - $210,000 annually, commensurate with experience. • We offer medical, dental, vision, 401k match, and unlimited PTO. • Candidates must be authorized to work lawfully in the United States.
Responsibilities
• Spend time with political strategists to understand how they think and what AI can do to improve their work.. • Translate that work into product decisions: how to structure workflows that involve humans and agents working together. • Help set the bar for rigor on what “good” looks like for an AI system, while staying pragmatic about what a real deadline and a real user actually need. • Design evaluations and LLM-as-judge systems that tell us whether an LLM-powered feature is accurate, well-grounded, and useful. • Prototype quickly, using coding agents to test ideas before they become product commitments. • Communicate findings and tradeoffs clearly to both technical and nontechnical audiences, including in client-facing settings.
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