Uber Freight
Logistics
AppliedScientistIII-ShipperPricing
Neural analysis suggests this role is
optimal for Senior candidates.
“Applied Scientist III - Shipper Pricing at Uber Freight. Skills: Machine learning, Causal inference, Optimization, Pricing algorithms. Develop algorithms for bidding. Trade off gross revenue”
What You'll Achieve.
Impact business metrics
Industry & Context.
Identify improvement opportunities; Analyze causal factors
What They're Looking For.
Must Have
M.S. or Bachelor's degree, 3+ years experience, Proficiency in A/B testing, Expertise in causal inference, Proficiency in Python, Proficiency in SQL, Proficiency in Spark
Nice to Have
Experience developing NN algorithms, Experience developing pricing algorithms, Familiarity with reinforcement learning, Familiarity with causal ML
What You'll Do.
Develop algorithms for bidding
Trade off gross revenue
Trade off net revenue
Analyze product performance
Identify improvement opportunities
Analyze causal factors
Establish standard methodologies
Provide recommendations
How You'll Work.
Team & Collaboration
Collaborate with Product; Collaborate with Operations; Collaborate with Engineering; Collaborate with scientists; Cross-functional teams
Communication Scope
Communicate findings; Provide recommendations
Full Job Description
Schedule: Full Time Employment Job Type: Hybrid Salary Type: Salary Req #:2538 About the Role As an Applied Scientist III 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 work closely with senior ICs to shape the 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 M. S. or Bachelor's degree in Computer Science, Machine Learning, or Operations Research, or equivalent technical background 3+ years of experience in developing and deploying machine learning models and optimization algorithms in production environments Proficiency in designing, launching, and analyzing A/B tests or other types of online experiments Expertise in observational causal inference or statistical analysis Proficiency in Python, SQL and Spark Preferred Qualifications Experience developing NN algorithms Experience
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