Job brief: Applied Scientist III - Shipper Pricing

What JoRoute checked · Checked by JoRoute

• Listed on Uber Freight's own job board. JoRoute does not take listings from job ads or recruiters.
• First seen by JoRoute 2026-11-04 (3 days ago); last confirmed on the board 2026-11-07.
• The apply link opens job-boards.greenhouse.io, the address Uber Freight's board gives for this job.
• No other listing with this title and place on this board in the last 90 days.
• No layoff notices (WARN) under this name in the state files JoRoute reads.

What the listing says · Employer's own words, not checked

• Pay: $124,600 to $151,950 a year
• Job type: Not stated
• Where: Chicago, IL 60607, United States · United States
• Posted: 2026-04-18 · over 60 days ago

What the job asks · Employer's own words, not checked

Level: Mid level. The posting asks for 3+ years of experience.

Must have

• M.S. or Bachelor's degree in Computer Science, Machine Learning, or Operations Research, or equivalent technical background
• 3+ years of experience in developing and deploying machine learning models and optimization algorithms in production environments

Conditions

• Job Type: Hybrid

Main duties

• 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…
• Collaborate with engineering teams to deploy, experimentally evaluate, and productionize these solutions.
• Communicate findings and insights to senior management and cross-functional teams.
• Track record of translating ambiguous business problems into technical solutions

Lines copied from the posting, not rewritten. "Preferred" or "nice to have" items are not listed as must-haves.

How it compares · Compared by JoRoute from listings

• Across Uber Freight's 67 open listings, 19% of those with a posting date were first posted more than 60 days ago. How Uber Freight hires

What nobody outside the employer knows · Not known

• Whether the role is already filled or on hold: employers often leave listings up.
• How many people applied, and who reads the applications.
• Where in the published range an offer would land.

Ask before you apply

1. Is this role still open, and when do you hope to fill it?
2. Which of the listed places is this role based in?
3. What are the interview steps, and when should I expect to hear back?

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Next steps

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