Head of Data & ML

Posh•New York, NY

6d•$300,000 - $350,000•Onsite

About The Position

The Head of Data & ML owns Posh’s entire data function and leads a unified team of eight data scientists, machine learning engineers, and analytics/data engineers. The role is accountable for the technical direction and excellence of event discovery for 1M monthly active users, experimentation and measurement that guide product investment, and the data platform the company relies on for decisions. It combines team leadership with hands-on technical work, setting direction, choosing high-impact problems, making sound decisions, and shipping work that advances the product. The Head translates product vision into a focused technical roadmap and helps the team make well-informed decisions. They contribute directly to analysis reviews, experiment critiques, model evaluations, and prototypes, while mentoring senior individual contributors into leaders and helping mid-level contributors develop. They establish a culture of rigor, urgency, and craft across data science, machine learning, and data engineering. The role advises on the development of ranking, recommendation, classification, and search systems, including problem framing, baselines, offline evaluation, and online testing. It defines measures of discovery quality, raises the rigor of experimentation and analysis, applies causal inference where A/B tests are insufficient, and improves understanding of marketplace dynamics and ecosystem tradeoffs. The Head also improves data infrastructure for AI/ML readiness, including the warehouse, feature pipelines, observability, and evaluation tooling, and makes build-versus-buy decisions. They lead development of a semantic layer and AI-native tools such as text-to-SQL, MCP, and agentic analytics, enabling employees to obtain trustworthy answers independently. They measure data adoption and address gaps, while setting expectations and development paths across the team. The role owns the data narrative at All Hands, leadership reviews, and board updates. This is an in-person role, five days per week at Posh’s SoHo, New York City headquarters. Relocation bonuses are available for candidates relocating from out of state.

Requirements

10+ years of experience in data.
3+ years managing data science, machine learning, and/or data engineering teams at a high-growth consumer or marketplace company.
Former Staff-level individual contributor with the technical ability to contribute to model reviews, experiment readouts, and architecture discussions.
Deep expertise in either causal inference or recommendation/ranking systems, and credibility in the other area.
Experience translating ambiguous product goals into technical roadmaps that shape critical decisions.
Belief in the importance of real-world human connection and passion for Posh’s mission.
In-person availability five days a week at Posh’s SoHo, New York City headquarters.

Nice To Haves

Experience building a semantic layer or AI evaluation frameworks in production.
Entrepreneurial experience as part of a 0-to-1 founding team or as a founder.
Experience working closely with a founder or CEO deeply involved in product, taste, and pace.

Responsibilities

Own the technical direction and overall data function, including discovery, experimentation and measurement, and the company data platform.
Translate product vision into focused data and machine learning priorities and a technical roadmap; explain feasibility, costs, learning opportunities, and paths to enable currently infeasible work.
Present the data narrative at All Hands, leadership reviews, and board updates.
Advise on ranking, recommendation, classification, and search algorithms from problem framing and baselines through offline evaluation and online testing.
Define metrics to assess improvements in event discovery.
Improve experiment design, metric selection, and analysis rigor; apply causal inference to questions A/B tests cannot answer cleanly.
Improve understanding of ecosystem tradeoffs and marketplace dynamics.
Improve data infrastructure for AI/ML readiness, including the warehouse, feature pipelines, observability, and evaluation tooling; ensure models and AI features use trusted data.
Make build-versus-buy decisions for data and ML infrastructure and tools.
Lead development of a semantic layer and AI-native tools, including text-to-SQL, MCP, and agentic analytics, so employees can access trustworthy answers independently.
Measure data adoption and address gaps.
Balance team leadership with hands-on technical work, including analysis reviews, experiment critiques, model evaluations, and building prototypes where useful.
Mentor senior individual contributors into leaders and help mid-level individual contributors develop.
Set expectations for excellent work across data science, machine learning, and data engineering, provide development paths, and address performance issues promptly and thoughtfully.
Lead a multidisciplinary team of eight data scientists, machine learning engineers, and analytics/data engineers.