Principal Data Scientist, Pricing

Los Angeles, CAFull-timePosted Jul 29, 2026

About the RoleWe're partnering with one of the largest freight and logistics companies in North America to reinvent how the trucking industry prices, bids, and captures value. This is a rare chance to work alongside a market leader with unmatched freight data depth, tackling pricing problems that have plagued carriers, brokers, and shippers for decades.As the Principal Data Scientist for this venture, you'll own the modeling and analytical intelligence behind our pricing product - building, validating, and iterating on the ML systems that power how our platform recommends and adapts freight pricing in real time. You'll partner closely with product and engineering, but your core mandate is the science: the models, the experiments, the insights that make the product defensible.

What You'll Do

Own the data science function for the venture, with freight pricing and revenue optimization as your primary domain
Build and iterate on ML models - dynamic spot and contract pricing, lane-level demand forecasting, load acceptance optimization, price elasticity, and market benchmarking
Design and run pricing experiments to validate model performance and surface actionable insights for product and commercial decisions
Partner with engineers to move models from prototype into production - providing guidance on deployment, monitoring, and model maintenance
Validate early business assumptions around freight pricing mechanics and contribute to the venture's monetization strategy with data-driven analysis
Establish data science best practices and model governance standards for the venture

What You Bring

8+ years of experience in data science and machine learning, with meaningful time spent on pricing, revenue optimization, or demand modeling
Demonstrated experience building and deploying ML models in production: dynamic pricing, price elasticity, willingness-to-pay, bid optimization, or similar
Strong ML and quantitative modeling background - whether grounded in data science, operations research, or systems engineering
Experience applying these skills to pricing, network optimization, supply/demand balancing, or marketplace dynamics in production environments
Familiarity with freight, logistics, or transportation data is a strong plus - lane economics, spot vs. contract dynamics, fuel surcharges, or carrier capacity signals
Comfort in ambiguous, early-stage environments where the data is messy, the roadmap is evolving, and you're expected to define the approach
Experience translating model outputs and tradeoffs into clear language for product, commercial, and executive stakeholders
Proficiency with standard data science tooling: Python, SQL, and relevant ML libraries

Nice to Have

Experience in freight or adjacent industries with similar pricing and network complexity: rideshare, airlines, ecommerce fulfillment, or digital marketplaces.
Familiarity with A/B testing frameworks for pricing experiments
Experience with reinforcement learning applied to dynamic pricing or sequential decision problems
Exposure to network optimization or capacity planning problems in logistics
Experience working with cloud data infrastructure (AWS, GCP, or Azure) and warehouse tooling (Snowflake, Databricks, dbt)