Director of Analytics

K Health•New York, NY

3d•$200,000 - $230,000•Hybrid

About The Position

K Health is looking for a Director of Analytics to lead their Strategic Insights Analysts and set the standard for AI-first decision-making across the company. This role is ideal for someone who pairs rigorous analytical judgment with a proven ability to build teams, systems, and operating models that scale insight generation far beyond any single analyst. The Director will manage and mentor Strategic Insights Analysts, own the analytics roadmap and prioritization, and partner with Product, Clinical Operations, Marketing, and Finance leadership to shape K Health's strategic direction. They will establish quality standards for data and metrics, define KPI ownership, and create a self-serve analytics strategy that gives stakeholders confidence in the answers they find. This role requires someone who can think like a top-tier business strategist, set standards like a data science leader, and build the team's operating model like an AI systems architect. They will define how SQL, Python, Looker, and AI agents combine to solve high-impact business problems at scale, while turning complex analyses into clear, actionable recommendations for executives. This is a high-impact leadership role for someone who thrives in a fast-paced, highly collaborative, and AI-enhanced environment. The Director will multiply their impact by raising the capability of everyone around them and by building durable systems that make reliable insight generation repeatable. This role requires onsite presence in their New York City office 4 days a week and does not provide immigration support.

Requirements

Analytics leadership: Have 7+ years in analytics, data science, strategy, consulting, or business operations; 2+ years managing high-performing analysts or analytics engineers; drive team-level AI productivity gains.
AI-first leadership: Design and operationalize AI agents, multi-agent orchestration, and LLM pipelines that measurably increase team output; set automation strategy, not just tool usage.
Analytical and metrics judgment: Bring expert SQL, data-modeling, KPI, and business-performance judgment; set query, validation, and metric standards; guide debugging and hypothesis testing, regressions, forecasting, and experimentation.
Systems thinking: Design scalable operating models, extracting one-off patterns into reusable documentation, skills, and standards.
Ownership and execution: Resolve ambiguous multi-system failures, make sound scope, sequencing, speed-fidelity, and tooling tradeoffs, and keep the team unblocked.
Teaching orientation: Raise capability through structured feedback, mentorship, and codified frameworks and documentation others use daily.
Communication through specification: Turn ambiguous questions into precise plans, tickets, and specifications for self-sufficient, asynchronous team execution.
Stakeholder management: Align Product, Clinical, Operations, Marketing, and Finance leaders on priorities, metrics, and tradeoffs; present to and challenge executives.
Learning agility: Rapidly master concepts, tools, AI technologies, and business models; lead the team's adoption curve.

Nice To Haves

Bring healthcare, health-tech, or regulated analytics experience with EMR/FHIR data, clinical workflows, and PHI-aware pipelines.
Use Looker/LookML, Tableau, or Mode to review LookML MRs and set explore-design standards.
Build or deploy production multi-agent AI systems, LLM batch pipelines (e.g., Vertex AI/Gemini), or prompt-engineering frameworks.
Work with Google Cloud's BigQuery, Vertex AI, and Workspace APIs plus git, code review, and PDT/dbt-style modeling.
Design analytics-team interview loops, hiring rubrics, and onboarding programs.
Bring prior management consulting, strategy, or business operations experience.

Responsibilities

Own the analytics roadmap, translating strategy into prioritized analyses, models, and self-serve products across Product, Clinical Operations, Marketing, and Finance
Drive AI-first operations by deploying agents, orchestration, LLM pipelines, and automation that increase analytical output
Lead self-serve analytics and Looker strategy, setting standards, maintaining semantic-layer quality and designing effective dashboards and visualizations
Define key performance metrics, automate monitoring, anomaly detection, and insight generation, and deliver high-signal leadership insights
Elevate team output through code review, teaching feedback, and reusable skills, playbooks, and establishing a high standard for output.
Translate complex analyses into clear actionable insights that drive decision making cross-functionally
Coordinate dependencies with data engineering, and evolving data infrastructure in parallel with evolving business needs