Pricing Data Scientist

EZCORP•Midland, TX

4d•Onsite

About The Position

The Pricing Data Scientist serves as the highest-level expert in pricing science, responsible for leading the design, development, and deployment of pricing data collection, AI-driven pricing models, machine learning, and Gen AI solutions that determine how every item is priced across all merchandise categories. This role owns the pricing model portfolio and pricing data acquisition process end to end — from foundational pricing models that cover all categories to specialized models for categories with distinct pricing dynamics, such as luxury purses and handbags, and electronics and tools. AI and systems programming are both foundational to this role: the Pricing Data Scientist applies data collection and curation, machine learning, Gen AI, and LLM-based techniques to product matching, aliasing and MDM data cleanup, price optimization, and pricing experimentation. This role’s contributions guide the company’s pricing roadmap and ensure scalable, ethical, and high-quality pricing solutions. This role involves close cross-functional collaboration with the Pricing Product Manager, Pricing Analysts, Data Engineering, Earning Assets, and executive leadership to identify pricing opportunities, validate hypotheses through controlled experiments, and operationalize pricing insights at scale.

Requirements

Bachelor's or higher level degree in computer science or a related field with 15+ years professional software development experience.
5+ years of experience in data science, analytics, or machine learning roles, including hands-on experience building pricing, demand, or revenue optimization models.
5+ years of experience in pricing data collection and/or product data collection.
Proven experience leading large-scale data acquisition or ML projects end-to-end, from research through production deployment.

Nice To Haves

Experience applying Gen AI and LLMs to production use cases (e.g., entity matching, data cleanup, classification) strongly preferred.
Experience with pricing experimentation — A/B testing, pilot design, and causal inference — strongly preferred.
Retail, e-commerce, resale, or secondhand-goods pricing experience a plus.

Responsibilities

Lead the design, development, and deployment of AI-driven pricing models covering all merchandise categories, including retail, DIP (buy/loan), and Historic Pricing methods.
Build data acquisition and curation pipelines to provide essential training and validation data for pricing models.
Build specialized pricing models for categories with distinct pricing dynamics — including luxury purses and handbags (brand- and model-level pricing) and electronics and tools (depreciation curves and external price benchmarking).
Apply Gen AI and LLM-based techniques to product matching, aliasing, and Master Data Management (MDM) cleanup to improve pricing data quality at scale.
Integrate external and competitive price data into pricing models to keep prices market-aligned.
Design and operationalize pricing experimentation infrastructure — pilots, A/B tests, monitoring, and trigger logic — to measure and validate pricing changes before and after release.
Automate the pricing pipeline in collaboration with Data Engineering, replacing manual pricing processes with scalable, monitored, AI-assisted systems.
Define optimized pricing methods and parameters per category and monitor their performance across margin, sales velocity, inventory turns, penetration, acceptance, and average price.
Partner with the Pricing Product Manager on pricing strategy: which categories need DIP prices tied to retail prices, which parameters drive Historic Pricing, and which categories require buffering.
Support the trigger-to-action workflow: perform deep-dive root cause analysis on pricing anomalies with Pricing Analysts, pulling supporting transactional data and classifying issues as data, aliasing, MDM, or methodology issues.
Conduct all aspects of descriptive, predictive, and prescriptive pricing analytics with sufficient rigor, testing, and documentation.
Oversee research into new pricing algorithms, AI frameworks, and data architectures.
Ensure pricing models and data science practices meet rigorous scientific, ethical, and engineering standards.
Effectively communicate complex pricing model results, delivery options, and expected business impact to business and executive stakeholders.
Collaborate with executives to guide data-driven pricing decisions and AI adoption.
Manage multiple priorities across a mix of ad hoc requests and projects.
Mentor and coach data scientists and pricing analysts; set standards for modeling practices, experimentation, and peer review.