Uber Freight

Logistics

AppliedScientistIII-ShipperPricing

$125–152k United States FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Applied Scientist III - Shipper Pricing at Uber Freight. Skills: Machine learning, Causal inference, Optimization, Pricing algorithms. Develop algorithms for bidding. Trade off gross revenue”

What You'll Achieve.

Impact business metrics

Industry & Context.

Logistics
Problems you'll solve

Identify improvement opportunities; Analyze causal factors

What They're Looking For.

Must Have

M.S. or Bachelor's degree, 3+ years experience, Proficiency in A/B testing, Expertise in causal inference, Proficiency in Python, Proficiency in SQL, Proficiency in Spark

Nice to Have

Experience developing NN algorithms, Experience developing pricing algorithms, Familiarity with reinforcement learning, Familiarity with causal ML

What You'll Do.

Develop algorithms for bidding

Trade off gross revenue

Trade off net revenue

Analyze product performance

Identify improvement opportunities

Analyze causal factors

Establish standard methodologies

Provide recommendations

How You'll Work.

Team & Collaboration

Collaborate with Product; Collaborate with Operations; Collaborate with Engineering; Collaborate with scientists; Cross-functional teams

Communication Scope

Communicate findings; Provide recommendations

Full Job Description

Schedule: Full Time Employment Job Type: Hybrid Salary Type: Salary Req #:2538 About the Role As an Applied Scientist III 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 work closely with senior ICs to shape the 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 assist quick product ideation and feature launch decisions. Basic Qualifications 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 Proficiency in designing, launching, and analyzing A/B tests or other types of online experiments Expertise in observational causal inference or statistical analysis Proficiency in Python, SQL and Spark Preferred Qualifications Experience developing NN algorithms Experience

Free ATS check

Applying for this Applied Scientist III - Shipper Pricing role?

Most applicants get filtered before a human reads their resume. See if yours makes the cut.

How to Apply on Greenhouse

  • Create a Greenhouse profile before applying — it saves time across multiple applications.
  • Upload your resume as a PDF; the parser handles it better than Word.
  • Answer all knockout questions carefully — wrong answers auto-reject before a human sees you.
  • Enable email notifications to track application status in real time.

ANONYMOUS · UNFILTERED

What do employees actually say about Uber Freight?

Real rants from real employees. Read before you apply.

Read Company Rants →