Staff Data Scientist - AI & Intelligence Systems

Search Atlas•San Francisco, CA

1d•$280,000 - $360,000•Hybrid

About The Position

Turn Data Into Decisions. Build Models That Ship. Lead the Science. Location: San Francisco, CA (Hybrid) The Opportunity Search Atlas powers autonomous marketing for Fortune 500s. Our AI agents make millions of optimization decisions daily - predicting search rankings, generating content, and managing ad spend without human intervention. We're not building dashboards. We're building the intelligence layer that replaces them. As our first Staff Data Scientist, you'll architect the models behind this agentic system. You'll own research roadmaps, mentor a growing team, and ship algorithms that directly drive revenue. No handoffs to "ML Engineering". You own the full lifecycle from notebook to production. Why this matters: We're bootstrapped and profitable. Every model you ship impacts our bottom line immediately. No VC pressure, no vanity metrics. What You'll Own You'll lead data science for one of three core systems: 🔮 Predictive SEO Intelligence Forecast rankings, predict algorithm shifts, and identify optimization opportunities before competitors. Build time-series models that process terabytes of crawl data. 🤖 Agent Decision Systems Power autonomous agent behavior through reinforcement learning, multi-armed bandits, and reward modeling. Your models decide what actions agents take and learn from outcomes. 🧠 Content & Entity Intelligence NLP systems for semantic analysis, entity extraction, content quality scoring, and generative optimization. Make LLMs production-reliable at scale. Your First Year Month 1-2: Deep dive into our data architecture (ClickHouse, streaming pipelines). Ship first optimization to existing models. Month 3-6: Lead design of new ML system. Define research agenda. Mentor junior team members. Month 6-12: Own strategic ML roadmap. Influence product direction. Establish technical standards for the data science org. We ship continuously, but we don't sacrifice rigor for speed. The Work Production ML Systems Build models serving millions of predictions/day with <100ms latency. Own full lifecycle: feature engineering, training, validation, A/B testing, drift monitoring. Design experiments that measure revenue impact, not just accuracy. Data Architecture Architect pipelines for terabyte-scale datasets (ClickHouse, PostgreSQL, streaming). Build evaluation frameworks that catch degradation before customers do. Research Leadership Define what we predict and optimize. Your scientific judgment sets priorities. Mentor data scientists and ML engineers through code review and research review. Translate complex methodologies into clear business impact for leadership. AI-Native Workflow Leverage Claude Code, Copilot, and custom agents to accelerate research. Build internal tools that democratize data science across the company.

Requirements

7+ years applied data science, with 3+ years shipping production ML systems.
Deep Python fluency: NumPy, Pandas, Scikit-learn, PyTorch or TensorFlow.
SQL at scale: Complex queries against terabyte datasets, query optimization.
ML Engineering mindset: Feature stores, model serving, A/B testing, monitoring, drift detection.
AI-native workflow: You use AI coding tools to accelerate research and automate analysis.
Scientific communication: You explain complex methodologies to executives with clarity.
Research leadership: You've mentored others, defined research agendas, shipped cross-functional projects.

Nice To Haves

NLP/LLM expertise
SEO/search domain knowledge
reinforcement learning
published research
Kaggle/GitHub presence

Responsibilities

Build models serving millions of predictions/day with <100ms latency.
Own full lifecycle: feature engineering, training, validation, A/B testing, drift monitoring.
Design experiments that measure revenue impact, not just accuracy.
Architect pipelines for terabyte-scale datasets (ClickHouse, PostgreSQL, streaming).
Build evaluation frameworks that catch degradation before customers do.
Define what we predict and optimize. Your scientific judgment sets priorities.
Mentor data scientists and ML engineers through code review and research review.
Translate complex methodologies into clear business impact for leadership.
Leverage Claude Code, Copilot, and custom agents to accelerate research.
Build internal tools that democratize data science across the company.