VP of Data & AI

BuyWander•Seattle, WA

just now•Hybrid

About The Position

BuyWander is rebuilding the $850B retail returns industry by transforming chaotic returned goods into an engaging shopping experience powered by AI, real-time auctions, and local curbside pickup. This role is for the first Head of Data & AI, a hands-on builder tasked with establishing the entire data function from scratch. The initial focus will be on building the foundational data infrastructure and high-leverage models, followed by building and leading a team to scale these efforts. The ideal candidate is a data "mad scientist" with a blend of architectural, quantitative, and business strategy skills, passionate about leveraging unstructured data to create marketplace liquidity.

Requirements

7+ years in data science, analytics, or ML engineering roles.
Proven track record of stepping into 0-to-1 environments.
Highly proficient in SQL, Python, and the modern data stack (e.g., dbt, Snowflake/BigQuery).
Experience actively using commercial APIs (OpenAI, Anthropic) or open-source models to build internal workflows and agents.

Nice To Haves

Experience with game theory to optimize auction mechanics.
Experience in product analytics and customer lifetime value modeling.
Experience with unstructured data ingestion and analysis.
Experience in operations analysis and optimization.
Experience building recommendation engines.
Experience with applied Generative AI and AI strategy.

Responsibilities

Design and build the modern data stack and pipelines from the ground up.
Partner with Finance and Ops to standardize reporting and deliver a trusted, scalable data foundation.
Use data and game theory to optimize auction mechanics, including starting bids, auction durations, bundling strategies, and dynamic pricing models.
Analyze user behavior to model Customer Lifetime Value (LTV), define "Bidder Value," and identify marketplace friction points.
Build models to ingest unstructured vendor manifests (CSVs, PDFs) to estimate yield and unit economics for purchasing decisions.
Measure and optimize warehouse operations, including ingestion times, grading accuracy, and labor efficiency.
Build recommendation engines for personalization and discovery, matching items to bidders.
Act as the internal vanguard for AI adoption, monitoring trends, testing models, and driving the organization's AI strategy.
Design and deploy applied AI microservices, such as an autonomous LLM agent for personalized email outreach.