Applied Scientist III

Uber Freight

Hybrid

Quick summary

Work type: Hybrid
Location: Chicago
Salary: $124,000–$152,000 / yr
Posted: today

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $170k

This role $138k

$97k most similar roles pay here $233k

This role pays less than 69% of similar roles. Most pay $130,989–$209,000 — the shaded band above. At the midpoint, this role pays about $138k versus about $170k 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 · Applied Scientist III

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As an Applied Scientist III at Uber Freight, you will join a dynamic team to develop and implement advanced statistical, machine learning, and optimization approaches to solve complex business problems. Your day-to-day responsibilities include building prototypes using causal inference, statistics, and machine learning algorithms, collaborating with engineering teams to productionize these solutions, and driving clarity in ambiguous business challenges through data-driven methods. You will also contribute to establishing standard methodologies for data science and design product experiments to inform decision-making. The role requires expertise in Python or R, SQL, and building data pipelines, along with a background in statistics, machine learning, operations research, economics, or computer science. Experience in time series analysis, pricing algorithms, and A/B testing is preferred.

Skills

Python R SQL AWS Kubernetes Terraform CI/CD Prometheus Grafana

What you'll do

Develop and implement advanced statistical, machine learning, and optimization approaches to solve complex business problems.
Build prototypes using algorithms based on causal inference, statistics, and machine learning to address business challenges.
Design product experiments and analyze results to draw detailed conclusions that inform decision-making.
Propose and guide robust frameworks for data analysis to drive business insights and improve performance metrics.
Collaborate with engineering teams to productionize solutions and create lasting impact in the organization.

What we're looking for

Bachelor’s degree in a relevant field such as Statistics, Machine Learning, or Computer Science.
3 years of experience developing statistical and machine learning models.
Expertise in causal inference, econometric, and statistical modeling techniques.
Proficiency in Python or R for model development and analysis.
Strong skills in SQL for data manipulation and analysis.
Experience building and managing data pipelines for model development.

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