Senior Applied Scientist - Shipper Pricing

Uber Freight

Hybrid

Quick summary

Work type
Hybrid
Location
San Francisco
Salary
$183,000–$223,200 / yr
Posted
today

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $169k
This role $203k
$120k most similar roles pay here $234k

This role pays more than 87% of similar roles. Most pay $146,500–$191,500 — the shaded band above. At the midpoint, this role pays about $203k versus about $169k for comparable roles.

Based on 240 similar postings.

Employer

About Uber Freight

Uber Freight is a logistics technology platform that connects shippers with carriers to simplify freight transportation, offering digital load booking, pricing transparency, and supply chain management tools.

Uber Freight currently has 11 open roles on FindRole.

Listed pay typically runs $149,750–$182,175 across 8 roles with salary data.

Most-posted roles

View all roles at Uber Freight

At a glance

TL;DR · Senior Applied Scientist - Shipper Pricing

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As a Senior Applied Scientist on the Shipper Pricing team at Uber Freight, you will leverage machine learning and optimization techniques to enhance real-time bidding algorithms for freight shipping across various auction types. Your daily tasks include developing innovative algorithms that balance gross and net revenue, prototyping solutions through statistical analysis and simulation, and collaborating with engineering teams to deploy and evaluate these models in production. You must have expertise in Python, SQL, and Spark, along with a strong background in machine learning and operations research. This role offers the opportunity to influence technical direction within a large-scale marketplace environment, addressing complex business challenges and driving measurable impact on key metrics.

Skills

Python SQL Spark Machine Learning Causal Inference Optimization Algorithms Neural Networks Reinforcement Learning CI/CD

What you'll do

  • Develop algorithms for real-time bidding on shipper freight to balance gross and net revenue.
  • Prototype and evaluate solutions using statistical analysis and simulation techniques.
  • Collaborate with engineering teams to deploy and experimentally evaluate pricing models.
  • Establish standard methodologies for data science practices in modeling, coding, analytics, and experimentation.
  • Influence technical direction by working closely with Product, Operations, Engineering, and other scientists.

What we're looking for

  • Ph.D. or M.S. in Computer Science, Machine Learning, or Operations Research.
  • 4+ years of experience developing and deploying machine learning models and optimization algorithms in production environments.
  • Expertise in observational causal inference and statistical analysis.
  • Experience with designing, executing, and analyzing experiments for ML model impact.
  • Proficiency in Python, SQL, and Spark.

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