Associate Director of Product Classification

YipitData

39d•Remote

About The Position

YipitData's Corporate team collaborates directly with organizations like Ulta, 3M, and Scotts Miracle-Gro to build one of the most comprehensive views into how consumers in the U.S. are purchasing and engaging with brands. By transforming massive volumes of consumer data into accurate, reliable, and actionable insights, we enable our clients to understand not just what is happening in their businesses, but why—and to use that understanding to make higher-confidence decisions in an increasingly competitive landscape. As part of our continued growth, we are expanding the Data team within our Corporate practice and are excited to welcome a Director of Product Classification to help deliver the most accurate category insights to our Retail and Brands clients. As an Associate Director, Product Classification, you will be the operational leader of our Product Classification organization — the team responsible for ensuring every product in our datasets is accurately categorized to meet the precise needs of our clients. You'll own the people, processes, and accountability frameworks that drive classification accuracy at scale, while maintaining excellent cross-functional partnerships with Engineering, Data, and our client-facing teams. This is a fantastic opportunity for someone who thrives in ambiguity & rapid growth. You will be hands-on in building and managing a multi-layered team — including a Data Classification and a Manual Data Quality team — and you'll be responsible for recruiting, training, and creating a culture of operational excellence and continuous improvement. You'll set the metrics that hold people accountable, build the systems that make the team scalable, and ensure our classification quality is best-in-class. This is a remote-friendly opportunity that can sit in NYC (where our headquarters is located), one of our office hubs, or anywhere else in the US. However, depending upon where the remote work is performed, income could be subject to New York State tax withholding. We expect East Coast work hours.

Requirements

8+ years of experience in data analytics, data operations, category mapping, or consulting, with at least 4+ years leading teams of 10+, and at least 2 years of experience managing team leaders.
2+ years of experience working with SQL, Python, PySpark, or other programming languages to explore and transform large, complex datasets; Familiarity with GitHub and Databricks
An operational excellence mindset — you build systems, metrics, and processes that scale. You hold people accountable without micromanaging.
A proven track record of hiring great talent, building training programs, and developing leaders within your team.
Comfortable with ambiguity and building from scratch — this is a team-building role. You'll be creating structure, not inheriting it.
Can clearly communicate complex concepts, both orally and in writing
A strong ownership mentality and are enthusiastic about making a big impact at a rapidly growing company

Nice To Haves

Data and classification domain experience (preferred) — experience with product taxonomies, categorization systems, e-commerce data, or similar domains is a strong plus.

Responsibilities

Own operational leadership and accountability for the Product Classification organization
Build and own the metrics framework that holds every team member accountable for classification accuracy, throughput, and quality.
Establish clear performance standards, SLAs, and escalation paths across the Data Classification and Data Quality teams.
Run a rigorous operating cadence — standups, reviews, retrospectives — that surfaces issues early and drives continuous improvement.
Recruit, train, and develop a high-performing classification team
Hire and onboard Data Classification Analysts who are obsessed with accurate outcomes and hungry to deeply learn what clients need in each category.
Build and scale your team, ensuring they can execute consistently on well-defined processes.
Create onboarding programs, training curricula, and knowledge bases that accelerate ramp time and reduce institutional knowledge risk.
Ensure analysts own the LLM prompt for each category, continuously refining prompts based on error patterns and client feedback.
Oversee the creation of scalable analytic checks that detect patterns of potential errors that neither the LLM nor manual checking can catch.
Ensure the team builds and maintains rules based on deep category expertise — key products, franchises, brands, and how they should be categorized.
Build and maintain a culture of operational excellence
Foster a team culture where accuracy is non-negotiable, curiosity is rewarded, and everyone takes ownership of outcomes.
Create an environment where Data Classification analysts are empowered to go deep — learning the nuances of client needs, franchise structures, brand hierarchies, and how products should be categorized.
Champion knowledge sharing across the team so that expertise scales beyond individual contributors.
Maintain excellent cross-functional relationships with Engineering, Insights, and Data to drive our mapping capabilities further. Partner with the Product Manager on system architecture and roadmap
Work closely with the PM (who owns the classification system architecture and vision) to ensure operational feasibility and team readiness for new initiatives.
Provide ground-level feedback on what's working, what's breaking, and where the biggest accuracy gaps exist to inform the PM's roadmap.
Ensure seamless handoff between the PM's process design and the team's execution.
Leverage AI to accelerate and improve our classification systems to be faster and more accurate
Partner with Engineering and the PM to continuously improve LLM-driven classification, using the team's domain expertise to train and evaluate models.
Identify opportunities where AI/automation can replace or augment manual work and lead the operational transition.
Stay current on advancements in NLP, LLMs, and classification technology to inform team strategy.