Senior Applied Scientist - Shipper Pricing
AI summary of the role
This Senior Applied Scientist role on the Shipper Pricing team at Uber Freight focuses on developing machine learning, causal inference, and optimization algorithms for real-time bidding on shipper freight across various auction types.
What you’ll do
- Develop creative algorithms for optimally trading off gross revenue and net revenue when bidding on shipper freight in real time across open auctions, sealed auctions, reverse waterfall auctions, etc.
- Prototype and evaluate solutions using statistical analysis and simulation.
- Collaborate with engineering teams to deploy, experimentally evaluate, and productionize these solutions.
- Leverage data to understand product performance and identify improvement opportunities, including analyzing potential causal factors.
What you’ll bring
- Ph.D. or M.S. in Computer Science, Machine Learning, or Operations Research, or equivalent technical background with exceptional demonstrated impact
- 4+ years of experience in developing and deploying machine learning models and optimization algorithms in production environments
- Experience with designing, executing and analyzing experiments to measure the impact of changes to production ML models
- Expertise in observational causal inference or statistical analysis
Technologies
Python · SQL · Spark · causal inference · optimization algorithms · reinforcement learning · neural networks · machine learning · experimentation
About Uber Freight (acquired Convoy)
Enterprise logistics platform combining TMS software, managed transportation, brokerage capacity and AI agents to run large shippers' freight networks across North America and Europe.
Public · 5000+ people
Source and classification
Internal deployment & tooling · Evidence for this classification:
Schedule: Full Time Employment Job Type: Hybrid Salary Type: Salary Req #: 2537 About the Role As a Sr. Applied Scientist on the Shipper Pricing team, you will apply machine learning, casual inference and optimization techniques to develop and improve Uber Freight’s algorithms for real time bidding on shipper freight. You will have a direct impact on Uber Freight’s key business metrics and the opportunity to heavily influence technical direction for this area. You will collaborate closely with Product, Operations, Engineering, and other scientists in the department on a daily basis. What the Candidate Will Do Develop creative algorithms for optimally trading off gross revenue and net revenue when bidding on shipper freight in real time across a variety of settings, e.g., open auctions, sealed auctions, reverse waterfall auctions, etc. Prototype and evaluate solutions using
More from the job description
Schedule: Full Time Employment Job Type: Hybrid Salary Type: Salary Req #: 2537 About the Role As a Sr. Applied Scientist on the Shipper Pricing team, you will apply machine learning, casual inference and optimization techniques to develop and improve Uber Freight’s algorithms for real time bidding on shipper freight. You will have a direct impact on Uber Freight’s key business metrics and the opportunity to heavily influence technical direction for this area. You will collaborate closely with Product, Operations, Engineering, and other scientists in the department on a daily basis. What the Candidate Will Do Develop creative algorithms for optimally trading off gross revenue and net revenue when bidding on shipper freight in real time across a variety of settings, e.g., open auctions, sealed auctions, reverse waterfall auctions, etc. Prototype and evaluate solutions using statistical analysis and simulation. Collaborate with engineering teams to deploy, experimentally evaluate, and productionize these solutions. Leverage data to understand product performance and identify improvement opportunities, including analyzing potential causal factors. Establish standard methodologies for data science, including modeling, coding, analytics, and experimentation. Communicate findings and insights to senior management and cross-functional teams. Provide recommendations to ass [... source excerpt omitted ...] or equivalent technical background with exceptional demonstrated impact 4+ years of experience in developing and deploying machine learning models and optimization algorithms in production environments, delivering measurable business impact over multiple quarters and making significant technical contributions Experience with designing, executing and analyzing experiments to measure the impact of changes to production ML models Expertise in observational causal inference or statistical analysis Proficiency in Python, SQL and Spark Preferred Qualifications Experience developing NN algorithms Experience in developing and deploying pricing algorithms for multi-sided real-time m [... source excerpt omitted ...] fficiently. We bring together the technology, people, and transportation capacity they need, using real‑time data from millions of shipments to guide smarter decisions. That helps customers spot issues early, avoid costly surprises, and deliver on time. Uber Freight works with 1 in 3 Fortune 500 shippers across North America and manages over $17B in freight. Learn more at www.uberfreight.com. Candidate Privacy Notice Uber Freight is committed to protecting the privacy of our candidates. We collect and process personal data in accordance with applicable data protection laws. For detailed information on how we handle candidate data, please review our Candidate Privacy Notice. EE
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