Pricing & Inventory Analyst

BuyWander•West Valley City, UT

4d•$60,000 - $70,000•Onsite

About The Position

BuyWander is rebuilding the $850B retail returns industry. We take chaotic truckloads of returned goods and transform them into a fast, fun, treasure-hunt shopping experience powered by AI, real-time auctions, and in-store pickup. Every night we list thousands of items whose retail reference price drives our recovery rates, our margins, and our customers' trust. You'll own the integrity of that pricing pipeline end to end: validating the manifest data we receive from suppliers, verifying what actually posts to the site, and forecasting what each truckload should earn - then flagging fast when reality diverges. You'll be the person who catches a mispriced load before it costs us margin, not after. This is a high-leverage, build-it-yourself role. We expect you to use modern AI tooling to work like a larger team - and to bring the discipline to verify everything it produces.

Requirements

Strong SQL and working proficiency in Python (or similar) for data validation, API integration, and automation.
Fluency with AI tooling as a force multiplier. You use Claude, Cursor, Copilot, or similar to build and iterate on validation logic, reconciliation scripts, and analyses far faster than convention - and you rigorously verify what they produce. In a role built on catching bad data, that skepticism has to extend to your own tools: a validation script that is subtly wrong is worse than none at all.
Experience reconciling large, messy third-party datasets - you assume data is wrong until proven right.
Experience with product or pricing data at scale (e-commerce, marketplace, or retail analytics); familiarity with Amazon catalog and pricing data (SP-API, Keepa, or similar) is a strong plus.
Ability to build forecasting models grounded in historical actuals - and to explain variance clearly to non-technical operators
A bias for automation: currently listing 10K items a night but needing to be prepared to 10X over the next 18 months, anything manual is already broken.
Comfort owning a metric end to end in a fast-moving, ambiguous environment.
Must reside and work in the United States and be legally authorized to work in the U.S. without current or future employer sponsorship.

Nice To Haves

Liquidation, reverse-logistics, or auction marketplace experience
Metabase or comparable BI-tool administration; dbt or similar transformation tooling
Experience building alerting and monitoring on data pipelines
A track record of using AI tooling to ship something a team would normally staff - with the receipts to show how you validated it
Success in This Role 100% of inbound manifests validated within 24 hours of receipt Listed-price error rate measured, baselined, and driven toward zero - with root causes fixed upstream Every load carries an expected-revenue forecast at listing, with forecast accuracy improving quarter over quarter Variance alerts that give Merchandising and Operations days of reaction time instead of a postmortem

Responsibilities

Validate inbound manifest data. Build automated checks on supplier manifests - item counts, UPC/ASIN matching, retail-price reasonableness, category mix - so bad data is caught before it prices a load.
Audit listed pricing at scale. Develop scripts that reconcile our listed prices against Amazon and other retailer reference prices daily across 10,000+ nightly listings, identifying systematic errors (wrong price basis, stale data, unit-of-measure issues) rather than one-offs.
Build variance reporting. Stand up dashboards and alerts (we run Metabase) that surface pricing anomalies and recovery-rate outliers by load, source, and market - within hours, not weeks.
Forecast load-level revenue. Develop expected-revenue models for each truckload based on manifest retail value, source, category mix, and historical recovery curves; track actual versus forecast as the load sells through, so under-performance is visible while we can still act on it.
Partner cross-functionally. Work with Merchandising on source scorecards and buy decisions, Finance on margin attribution, and Operations on loads whose forecast-to-actual gaps point to processing or inventory-control issues.