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Senior Applied Scientist

Uber Freight

$142,700 - $174,050
Apr 23, 2025
Chicago, IL 60607, US
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The company is looking to solve complex business problems and improve performance through advanced machine learning, statistical, economic, and optimization approaches.

Requirements

  • Strong knowledge of experimental design and analysis
  • Expertise in causal inference, statistical analysis and testing, and model development
  • Ability to use a programming language like Python or R to work efficiently at scale with large data sets
  • Proficiency in SQL, Hive, and Spark for data manipulation and analysis
  • Machine learning
  • Statistics
  • Operations Research

Responsibilities

  • 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.
  • Establish 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.

Other

  • Bachelor’s degree in Statistics, Machine Learning, Operations Research, Economics, Computer Science, or another related field
  • 5 years of related experience
  • Master’s or Ph.D. in Statistics, Machine Learning, Operations Research, Economics, Computer Science, or a related field
  • Communicate findings and insights to senior management and cross-functional teams
  • Provide recommendations to assist quick product ideation and feature launch decisions