Senior Data Scientist - Econometrics and Inference - Uber Freight
As a Data Scientist on the Carrier team at Uber Freight, you will play an instrumental role in the definition and development of our ambitious products - from Load Search, Ranking and Recommendation to Tracking/GPS and Automated Support. You are deeply technical, strategically minded, and can quickly toggle your mind between first-order problem-solving and modeling techniques. You are a great team player and enjoy working with a group of devoted cross-functional partners to "make things happen".
What You'll Do
Bring strong technical expertise to help drive profitability at Uber Freight
Drive innovation, generate and validate new data, tactics and product ideas that maximize our business impact
Be creative - relentlessly identify new ideas and data sources to solve very difficult problems
Feature engineering and analytics to identify and demonstrate the opportunities of modeling improvements
Drive cross-functional initiatives to set qualitative objectives and quantitative goals
Lead the creation of pragmatic, prioritized, actionable roadmaps that effectively distill vision and strategy into efficiently shipped results.
Basic Qualifications
Masters degree in operations research, computer science or a related quantitative field.
Strong expertise in developing and deploying large scale machine learning and optimization models in production
At least 5 years of industry experience in data science, with significant personal experience as a technical contributor.
Experience solving deep technical problems using optimization and statistical approaches.
Knowledge of experimental design and analysis
Experience with common analysis tools - Python, R, and SQL. Expert with code and programming concepts.
Preferred Qualifications
PhD degree in operations research, computer science or a related quantitative field
Experience working with spatial data, in revenue management, bidding, or in the logistics industry
Tech lead experience and passion for mentoring
Expertise in convex programming and computational optimization
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