Principal Data Scientist, Pricing
RemoteUnited States
Job Summary
Own the data science function for the venture, focusing on freight pricing and revenue optimization. Build and iterate on ML models for dynamic spot and contract pricing, lane-level demand forecasting, load acceptance optimization, and price elasticity. Design experiments to validate model performance and surface actionable insights for product and commercial decisions. Partner with engineers to deploy models into production, ensuring monitoring and maintenance. Validate early business assumptions around freight pricing mechanics and contribute to the monetization strategy with data-driven analysis. Establish data science best practices and model governance standards. Requires 8+ years in data science and machine learning with production deployment experience.
Required Qualifications
- 8+ years of experience in data science and machine learning
- 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
- similar
- Strong ML and quantitative modeling background
- data science
- operations research
- systems engineering
- Experience applying these skills to pricing, network optimization, supply/demand balancing, or marketplace dynamics in production environments
- proficiency with standard data science tooling
- Python
- SQL
- relevant ML libraries
Desired Qualifications
- Familiarity with freight, logistics, or transportation data
- lane economics
- spot vs. contract dynamics
- fuel surcharges
- carrier capacity signals
- Comfort in ambiguous, early-stage environments
- data is messy
- roadmap is evolving
- expected to define the approach
- Experience translating model outputs and tradeoffs into clear language for product, commercial, and executive stakeholders
- Experience in freight or adjacent industries with similar pricing and network complexity
- rideshare
- airlines
- ecommerce fulfillment
- 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
- Azure
- warehouse tooling
- Snowflake
- Databricks
- dbt
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