Applied Scientist III
@ Uber Freight

Chicago, Illinois, United States
On Site
Posted 4 days ago

Your Application Journey

Personalized Resume
Apply
Email Hiring Manager
Interview

Email Hiring Manager

XXXXXXXXX XXXXXXXXXXXXX XXXXXX****** @uberfreight.com
Recommended after applying

Job Details

About the Role

As an Applied Scientist III at Uber Freight, you will develop and implement advanced machine learning, statistical, economic, and optimization approaches to solve complex business problems and improve performance. You will apply modeling skills and data-driven methods to enhance our core product experience and top line business metrics. You’ll collaborate closely with Product, Operations, and Engineering teams to turn insights into impact.

What the Candidate Will Do

The role includes:

  • Developing creative solutions and prototypes using ML, causal inference, statistics, and optimization.
  • Collaborating with engineering and product teams to productionize solutions.
  • Driving clarity in ambiguous business problems using data-driven approaches.
  • Proposing robust frameworks for data analysis to drive insights.
  • Contributing to standard methodologies for modeling, coding, analytics, and experimentation.
  • Leveraging data to assess product performance and identify opportunities.
  • Designing product experiments and interpreting results.
  • Communicating findings to senior management and cross-functional teams.
  • Providing recommendations for rapid product ideation and feature launches.
  • Building intelligent, data-driven products for improved user experience.

Basic Qualifications

  • Bachelor’s degree in related field (Statistics, ML, Operations Research, Economics, Computer Science, etc.).
  • 3 years of related experience.
  • Proficiency in Python or R for model development and analysis.
  • Proficiency in SQL for data manipulation and analysis.
  • Experience building and managing data pipelines.
  • Experience in designing, launching, and analyzing A/B tests and online experiments.

Preferred Qualifications

  • Master’s or Ph.D. in a relevant field.
  • Strong knowledge of experimental design and analysis.
  • Expertise in observational causal inference or statistical analysis.
  • Experience with Spark for large-scale data manipulation.
  • Experience in production environments on fraud, recommendation, or dynamic pricing algorithms.

Benefits & Compensation for U.S. Employees

Eligible US employees working over 30 hours at Uber Freight will have access to a company sponsored health plan, dental and vision benefits, 401k match, financial and mental wellness benefits, parental leave, short- and long-term disability, life insurance and more. Additional incentive and equity award programs may also be available.

About Uber Freight

Uber Freight is a market-leading enterprise technology company powering intelligent logistics. It manages nearly $20B of freight and operates one of the largest carrier networks, serving 1 in 3 Fortune 500 companies. For more, visit www.uberfreight.com.

Candidate Privacy Notice & EEOC

Uber Freight is committed to candidate privacy, collecting and processing data in accordance with applicable laws, and is an Equal Opportunity/Affirmative Action employer.

Key skills/competency

  • Machine Learning
  • Statistical Analysis
  • Optimization
  • Causal Inference
  • Data Modeling
  • SQL
  • A/B Testing
  • Python
  • Data Pipelines
  • Spark

How to Get Hired at Uber Freight

🎯 Tips for Getting Hired

  • Research Uber Freight's culture: Understand their mission and logistics innovations.
  • Tailor your resume: Highlight relevant ML and statistics projects.
  • Showcase technical skills: Emphasize Python, SQL, and data pipeline experience.
  • Prepare for interviews: Practice problem-solving and A/B test design.

📝 Interview Preparation Advice

Technical Preparation

Review Python and R libraries.
Practice SQL queries and data manipulation.
Brush up on Spark and pipeline design.
Study ML and optimization algorithms.

Behavioral Questions

Describe a time you solved ambiguity.
Explain a cross-team collaboration experience.
Discuss handling challenging data problems.
Share how you communicate technical insights.

Frequently Asked Questions