Applied Scientist III

About the Role 

As an Applied Scientist III at Uber Freight, you will develop and implement advanced machine learning, statistical, economic, and optimization approaches to solve complex business problems and improve performance. You will apply modeling skills and data-driven methods to enhance our core product experience and top line business metrics. You’ll collaborate closely with Product, Operations, and Engineering teams to turn insights into impact. 

What the Candidate Will Do 

• Develop creative solutions and build prototypes to business problems using algorithms based on machine learning, causal inference, statistics, and optimization. 


• Collaborate with engineering and product teams to productionize these solutions and create a lasting impact. 


• Drive clarity and solve ambiguous, challenging business problems using data-driven approaches, with an emphasis on understanding causal relationships when applicable. 


• Propose and guide robust frameworks of data analysis to drive business insights and inform decision-making. 


• Contribute to establishing standard methodologies for data science, including modeling, coding, analytics, and experimentation. 


• Leverage data to understand product performance and identify improvement opportunities, including analyzing potential causal factors. 


• Design product experiments and interpret results to draw detailed and impactful conclusions. 


• Communicate findings and insights to senior management and cross-functional teams. 


• Provide recommendations to assist quick product ideation and feature launch decisions. 


• Build intelligent data-driven products to provide the best user experience. 

Basic Qualifications 

• Bachelor’s degree in Statistics, Machine Learning, Operations Research, Economics, Computer Science, or another related field 


• 3 years of related experience 


• Demonstrated proficiency in programming languages like Python or R for purposes of model development and analysis 


• Proficiency in SQL for data manipulation and analysis 


• Proficiency in building and managing data pipelines for purposes of model development or analysis 


• Proficiency in designing, launching, and analyzing A/B tests or other types of online experiments 

Preferred Qualifications 

• Master’s or Ph.D. in Statistics, Machine Learning, Operations Research, Economics, Computer Science, or a related field 


• Strong knowledge of experimental design and analysis 


• Expertise in observational causal inference or statistical analysis  


• Ability to use a programming language like Python or R to work efficiently at scale with large data sets 


• Proficiency in Spark for data manipulation and analysis at scale 


• Experience working on fraud, recommendation or dynamic pricing algorithms in a production environment