Uber Freight
Logistics
SeniorAppliedScientist-ShipperPricing
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Applied Scientist - Shipper Pricing at Uber Freight. Skills: Data science, Machine learning, Data engineering. Develop and deploy machine learning models. Design and implement data pipelines”
Industry & Context.
Problem solving; Analytical skills
What They're Looking For.
Must Have
4+ years of experience, Master's degree in Statistics, Computer Science, Mathematics, or related quantitative field, Proficiency in SQL
Nice to Have
PhD preferred, Experience with scikit-learn, TensorFlow, PyTorch, XGBoost, or LightGBM, Experience with AWS, GCP, or Azure cloud platforms, GCP Professional Data Engineer certification, AWS Data Analytics certification, Databricks Certified, Dbt Certified
What You'll Do.
Develop and deploy machine learning models
Design and implement data pipelines
Build and maintain data infrastructure
Perform statistical analysis
Develop predictive models
Collaborate with engineering teams
Communicate findings to stakeholders
How You'll Work.
Team & Collaboration
Cross-functional teams; Engineering teams; Stakeholders
Full Job Description
Schedule: Full Time Employment Job Type: Hybrid Salary Type: Salary Req #: 2537 About the Role As a Sr. Applied Scientist on the Shipper Pricing team, you will apply machine learning, casual inference and optimization techniques to develop and improve Uber Freight’s algorithms for real time bidding on shipper freight. You will have a direct impact on Uber Freight’s key business metrics and the opportunity to heavily influence technical direction for this area. You will collaborate closely with Product, Operations, Engineering, and other scientists in the department on a daily basis. What the Candidate Will Do Develop creative algorithms for optimally trading off gross revenue and net revenue when bidding on shipper freight in real time across a variety of settings, e.g., open auctions, sealed auctions, reverse waterfall auctions, etc. Prototype and evaluate solutions using statistical analysis and simulation. Collaborate with engineering teams to deploy, experimentally evaluate, and productionize these solutions. Leverage data to understand product performance and identify improvement opportunities, including analyzing potential causal factors. Establish standard methodologies for data science, including modeling, coding, analytics, and experimentation. Communicate findings and insights to senior management and cross-functional teams. Provide recommendations to assist quick product ideation and feature launch decisions. Basic Qualifications Ph. D. or M. S. in Computer Science, Machine Learning, or Operations Research, or equivalent technical background with exceptional demonstrated impact 4+ years of experience in developing and deploying machine learning models and optimization algorithms in production environments, delivering measurable business impact over multiple quarters and making significant technical contributions Experience with designing, executing and analyzing experiments to measure the impact of changes to production ML models Expertise in observationa
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