Product Manager III

ACV•Buffalo, NY

About The Position

We’re hiring a Product Manager to lead our Data & Search platform, with a focus on leveraging industry leading data capabilities to improve how buyers discover inventory and how sellers maximize performance. As a Product Manager for our Vehicle Intelligence Team you will you will sit at the intersection of: Auto Tech / Automotive Marketplace dynamics (supply, demand, pricing), Search & ranking systems, Data products (vehicle intelligence, enrichment), and Machine learning models (ranking, pricing, recommendations). This role directly impacts conversion, liquidity, and revenue optimization across the platform. This role sits at the core of building a data-driven competitive advantage by ensuring the platform has the highest quality, most complete, and most actionable data foundation in the market. While machine learning enables optimization, it is the breadth, accuracy, and structure of the underlying data that ultimately determines performance. You will drive how vehicle, pricing, and behavioral data are sourced, enriched, standardized, and made usable across the ecosystem—powering everything from search relevance to pricing confidence and marketplace liquidity. By improving data coverage, reducing inconsistency, and unlocking new signals, you enable faster, more reliable decision-making for both buyers and sellers, creating a compounding advantage that competitors cannot easily replicate.

Requirements

4+ years industry (Auto-tech) / Product Management experience
Experience owning data-heavy or technically complex product areas, including data pipelines, schema and event design, platform integrations, and prioritizing product investments that improve data accessibility, usability, and business impact.
Strong understanding of modern data hygiene practices, including data quality management, governance, lineage, validation, and the processes required to ensure trusted, reliable, and scalable data assets across the organization.
Demonstrated success in identifying and implementing LLM-based solutions to augment user workflows, resulting in significant efficiency gains or enhanced capabilities.
Proven track record of leading the end-to-end delivery of complex, technical products, from ideation through to launch and iteration, consistently driving measurable business impact.
Ability to drive product strategy by translating complex data into actionable insights, which requires a strong quantitative toolkit including hands-on proficiency in SQL, rigorous experimentation design, and a deep understanding of metrics architecture
Experience working cross-functionally with engineering teams
Agile experience and release planning prioritization skills
Presentation skills - Ability to simplify complex data and technical systems
Mentoring and coaching skills
Bachelor’s degree in a relevant field (e.g., Computer Science, Engineering, Data Science, or similar). Advanced degrees are a plus.

Responsibilities

Own data products powering ML models, such as vehicle attributes, condition, and history.
Lead as the connective tissue between Engineering, Data Science, ML teams, and Commercial stakeholders.
Collaborate with engineering and data science teams on ML model feature definition and selection, ensuring inputs align with product goals, customer behavior, and measurable business outcomes.
Collaborate with engineering and data science teams to define, evaluate, and improve model performance metrics aligned to product outcomes and customer value.
Employ a hypothesis-driven approach to product development, converting ambiguity and assumptions into a clear framework for experimentation and validation via experimentation frameworks, including A/B testing, and evaluating offline versus online metrics to measure product and model performance.
Ensure data quality, coverage, and the reliability of underlying models and services that power critical product experiences and business decisions.
Translate complex data from multiple sources—including user behavior, seller performance, and model outputs—into actionable insights that drive product prioritization and innovation.
Improve inventory liquidity, pricing accuracy to drive trust and optimize the marketplace.
Identify high-leverage product opportunities through ranking improvements, data enrichment, and better use of signals to improve relevance, conversion, and customer outcomes.
Guide product strategy by evaluating and clearly communicating critical trade-offs between technical implementation and business value (e.g., performance vs. explainability).
Define and own a 6–12 month roadmap across Data, Search, and ML-driven features.
Articulate and advocate for product strategy, roadmap, and business outcomes to executive leadership to secure buy-in and alignment.