Principal Data Scientist, Pricing

Location: Los Angeles, CA

Department: Engineering

Location Type: REMOTE

Employment Type: FULL_TIME

• 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

• 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

• 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)