Junior Data scientist

We're Cargo, a fast-growing venture-backed retail tech startup based in NYC with an in-car commerce platform exclusively distributing products for brands like Mars/Wrigley, Kellogg's, RX Bar, and Red Bull. Need something while on the go? Grab it from Cargo at arms-length convenience and benefit your driver in the process.

Cargo is continuing to expand across the country, and in a matter of a few years, we'll have more store-fronts than any other chain the U.S. As part of the data science team, you'll help guide our rapid growth by producing the reports and tools our company uses to make data-driven decisions. You'll regularly meet with various Cargo teams such as Driver Operations, Logistics, Merchandising, Sales, and Finance to help break down complex data sets into meaningful insights and help solve challenging problems that come with tracking and replenishing inventory across thousands of stores with millions of transactions each year, incentivizing how gig-economy workers monetize their time, and influencing in-car behavior of passengers spending 2 billion hours per year in rideshare cars. 



  • Meet with other teams to determine what reports and insights they need and communicate feasibility for those needs
  • Develop reports, dashboards, and data access tools that make it easier for Cargo's teams to make data-driven decisions
  • Develop a strong sense of ownership over the accuracy of the reports you provide to the team
  • Participate in peer code reviews
  • Work closely with the software development team to capture additional data sources used for analyses 



  • A degree in computer science, computational sciences, mathematics, or equivalent experience 
  • 1-2 years in a data science role 
  • Strong proficiency with SQL and the ability to solve novel segmentation, aggregation, and visualization problems from scratch
  • The ability to build reports that other teams request plus the initiative to proactively develop additional insights for them
  • Excellent communication skills to understand needs from other teams
  • Familiarity with NumPy, SciPy, Pandas, or Scikit-learn is a nice-to-have but not necessary


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