Data Scientist, Kaizo

• A solid understanding of how LLMs work and hands-on experience prompting them for accuracy (coursework, thesis, internships, or side projects all count; production experience is a bonus)
• Good applied statistics: experiment design, classifier evaluation, precision/recall trade-offs, and working with heavily imbalanced data
• Strong Python skills and fluency with the standard data toolkit (Pandas, NumPy, Jupyter). SQL is a plus
• An analytical, evidence-first mindset: you'd rather measure than assume
• Product sense and empathy for end users. You'll be building for QA managers and support agents, not just for benchmarks
• Excellent written and verbal communication. You'll present findings to the team and join customer conversations
• 0 to 2 years of industry experience. Recent graduates with strong relevant work are encouraged to apply
• A team player who's comfortable wearing multiple hats. We're an early-stage company and things move fast

Desirable

• We're looking for a sharp, analytical data scientist to own the evaluation and quality loop of our AutoQA product. You can be early in your career (recent graduates with strong LLM fundamentals are welcome), we have room and a clear path for you to grow
• Translate customer rubrics into AutoQA instructions. Work with real customer quality criteria and turn them into precise, testable instructions that LLMs can score reliably
• Run experiments that move accuracy. Design and execute evaluation experiments on large, representative datasets using LangSmith and BigQuery, and track quality with our performance metrics
• Build the datasets that make evaluation possible. Curate raw production data into golden datasets with balanced coverage, and generate synthetic data to cover the rare cases that matter most. QA is a discipline of rare occurrences: distributions are skewed, positives are scarce, and resourcefulness beats volume
• Build LLM-as-a-judge pipelines to assess system quality internally and make evaluation repeatable
• Make results actionable. Your experiments should end in a decision: change a prompt, adjust which tools the system uses, surface context the AI is missing, or flag where new capabilities are needed. You'll work with the AI team to ship those decisions
• Evaluate across the full stack. Beyond scoring quality, you'll help validate retrieval (RAG/IR), tool calling, and speech pipelines (transcription and diarization quality)
• Join customer calls with the team to understand how QA leaders define quality, and feed what you learn back into the product
• What you'll grow into:
• Shaping how customers monitor quality themselves, catch drift, and keep their AutoQA setup improving over time
• Smarter categorization and routing of conversations to power analytics and get the right tickets to the right evaluation
• You'll join our AI team (two data scientists, an AI engineer, and an ML engineer) and collaborate closely with our data engineers, frontend engineers, designer, product manager, and CX teams. You'll have mentorship from day one and real ownership fast