About the Role:
We'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.
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)
