Data Scientist III
Uber Freight · Hyderabad, Telangana, India
Transportation, Logistics, Supply Chain and Storage · 5,001-10,000 employees
About the role
The Data Scientist will architect analytics roadmaps and deploy machine learning models to optimize logistics workflows and drive operational excellence. They will collaborate with cross-functional teams to synthesize data and quantify the financial impact of process improvements.
What they look for
Requirements
Candidates must have a bachelor's degree in a quantitative field and at least 3 years of relevant experience. Proficiency in SQL and Python is required, with preference given to those with experience in supply chain domains and advanced statistical methods.
Full description
Schedule: FT
Job Type: Hybrid
Salary Type: Salary
Req #: 2017
About the Role
We are seeking an exceptionally passionate person to join our Data Science team to architect the future of Uber Freight Analytics. In this role, you will go beyond standard reporting to drive an analytics roadmap that unlocks Operational Excellence (OpEx) across the business. You will deliver advanced descriptive and predictive solutions to Operations, Finance, and Logistics leadership, leveraging statistical modeling and machine learning to optimize logistics workflows, identify cost-saving opportunities, and provide the groundwork for AI-driven automation. You will collaborate across functions to solve complex problems, create massive operational efficiencies, and ensure every initiative is backed by a rigorous, data-driven impact attribution.
What the Candidate Will Do
- Perform deep-dive exploratory and statistical analysis to uncover hidden inefficiencies in global logistics networks and deploy ML models that drive automated decision-making.
- Apply advanced statistical analysis to identify OpEx opportunities and provide the data-driven insights necessary to architect and facilitate large-scale AI implementations.
- Develop robust attribution frameworks to quantify the financial and operational impact of process improvements, ensuring alignment with core company priorities and cost-reduction targets.
- Collaborate with cross-functional teams such as product, engineering, and operations to drive system development end-to-end from conceptualization to productization
- Wrangle and synthesize fragmented data from multiple providers and internal systems to create a 'single source of truth' for operational performance
Basic Qualifications
- Bachelor’s degree in Statistics, Mathematics, Computer Science, or related field
- 3 years of related experience
- Experience with SQL, Python
Preferred Qualifications
- Masters with 3 years of experience or Bachelors with 5 years of relevant experience
- Experience with orchestration tools (e.g., Airflow)
- Experience with Spark
- Advanced knowledge of experiment design and statistical methods
- Strong presentation skills
- Experience working in a supply chain / logistics domain
About Uber Freight
Uber Freight helps companies move goods more reliably and efficiently. We bring together the technology, people, and transportation capacity they need, using real‑time data from millions of shipments to guide smarter decisions. That helps customers spot issues early, avoid costly surprises, and deliver on time. Uber Freight works with 1 in 3 Fortune 500 shippers across North America and manages over $17B in freight. Learn more at www.uberfreight.com.
Candidate Privacy Notice
Uber Freight is committed to protecting the privacy of our candidates. We collect and process personal data in accordance with applicable data protection laws. For detailed information on how we handle candidate data, please review our Candidate Privacy Notice.
EEOC
Uber Freight is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regards to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
