Chicago, IL 60607, United States

Data Scientist III - Spot Pricing

USD 99K–151K / yearMidData and machine learning

PythonSQLAirflowSparkStatistical analysis

Original posting ↗Apply ↗Company job board ↗Follow new Python jobs in Telegram

Data Scientist III - Spot Pricing is an open role at Uber Freight, a company in logistics. The job is based in Chicago, United States. Our labels mark it as a mid-level role in data and machine learning. Technologies named in the posting: Airflow, Spark, Python and SQL. It asks for 3+ years of experience. Visa sponsorship is not offered. A university degree is required. Uber Freight has 56 open positions on this site, 6 of them in IT and engineering.

Posted today · Last checked 2026-11-04

About Uber Freight

Logistics Non-tech company with an IT team 56 open positions, 11% in IT and engineering. Most open roles: Operations and admin (19), Customer success and support (10), Finance and legal (9).

All open jobs at Uber Freight →

Similar jobs

People Research ScientistCrusoe · Denver, CO - USUSD 140K–170K / year Founding Machine Learning Engineer, Computer Vision (Object Detection)Clera · New YorkUSD 200K–260K / year Founding Engineer, AI/MLClera · San FranciscoUSD 130K–170K / year Production Support Engineer - LLM PlatformQube Research & Technologies · New York, Chicago Staff Analytics EngineerPlayStation Global · United States, San Mateo, CAUSD 223.5K–300.1K / year Business Intelligence DeveloperNT Concepts · Chantilly, VA; Vienna, VAUSD 120.3K–180.5K / year Applied Data Science Managerdunnhumby · Bentonville; Cincinnati Business Intelligence EngineerPenn Interactive · Remote, United States USD 80K–115K / year

1,261 open data and machine learning jobs in the United States All open jobs at Uber Freight →

Workplace	Workplace not stated
Seniority	Mid
Role	Data and machine learning
Countries	United States
Languages	PythonSQL
Key facts	Stack: Python, SQL, Airflow, Spark · Experience: 3+ years · Degree required · Visa sponsorship: no

The full description is on the original posting. Labels are set automatically and can be wrong. About the data