Our client is a VC-backed, AI-first SaaS company headquartered in San Francisco. They’ve moved past MVP and are scaling revenue with enterprise customers in finance and healthcare. To support a fast-growing roadmap of search, summarization, and agent features, they’re hiring a Staff Data Scientist who can turn noisy product ideas into reliable, measurable GenAI outcomes.
The role at a glance
You’ll be the go-to partner for Product & Engineering on GenAI features—owning experiment design, retrieval quality, and model evaluation so launches are predictable and costs stay under control.
What you’ll do
Translate business problems into testable ML/GenAI hypotheses; define success metrics and guardrails.
Build and tune RAG pipelines (chunking, embeddings, vector DB), plus offline/online eval suites (relevance, groundedness, human-in-the-loop).
Design and run A/B experiments and interleaving tests; communicate results in clear decision memos.
Ship DS artifacts to production with Eng (batch/stream pipelines, feature store, monitoring).
Track latency/cost/quality budgets; propose routing/fallbacks across OSS and commercial models.
What you’ll bring
6–10+ years in DS/ML, including 2–3+ shipping LLM/RAG or search/ranking at scale.
Strong Python/SQL; experimentation design and causal inference basics.
Comfort moving from notebook to prod; crisp stakeholder communication.
STEM MS/PhD preferred.
Package (guide)
$200k–$260k base + bonus/equity; benefits; hybrid SF.