Senior Applied Scientist - Shipper Pricing

Uber Freight

Hybrid

Quick summary

Work type
Hybrid
Location
Chicago
Salary
$152,500–$186,000 / yr
Posted
today

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $169k
This role $169k
$123k most similar roles pay here $202k

This role pays more than 50% of similar roles. Most pay $146,500–$191,500 — the shaded band above. At the midpoint, this role pays about $169k 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.

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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 develop algorithms for real-time bidding in various auction settings. Your daily tasks include prototyping solutions through statistical analysis, collaborating with cross-functional teams to deploy and evaluate these models, and establishing methodologies for data science practices. The role requires expertise in Python, SQL, and Spark, along with a background in machine learning or operations research. You will work on complex technical projects, influence the direction of pricing algorithms, and solve ambiguous business problems by translating them into actionable technical solutions.

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 across various auction types.
  • 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 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 in production environments.
  • Expertise in observational causal inference and statistical analysis.
  • Experience with designing and analyzing experiments for production ML models.
  • Proficiency in Python, SQL, and Spark.

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