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We continually seek opportunities to expand our network of talented professionals, whether as part of NEA's team of investors and support personnel, or in an exciting role at one of our portfolio companies. Open positions with NEA will be posted as available, as well as a listing of current career opportunities with NEA-backed companies.

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Job Details

Engineering Manager, Data Science Workbench at Uber
San Francisco, CA, US

At Uber, we ignite opportunity by setting the world in motion. We take on big problems to help drivers, riders, delivery partners, and eaters get moving in more than 600 cities around the world.

 

We welcome people from all backgrounds who seek the opportunity to help build a future where everyone and everything can move independently. If you have the curiosity, passion, and collaborative spirit, work with us, and let’s move the world forward, together.

About the role

 

Uber is currently looking for an experienced engineering manager to lead Uber’s Data Science Workbench (DSW) development effort. Based in Palo Alto, the team is responsible for building a Data science & Machine learning platform that’s used by thousands of Uber’s Data scientists and analysts for ad hoc data exploration and experimental Machine learning and Deep learning. The team’s mission is to unleash the productivity of Data scientists and democratize Machine learning and analytics by providing a reliable, secure, and user friendly platform. DSW brings together the best of data processing, analysis, Machine learning, and visualization tools (open source, in-house and 3rd party proprietary) under a single pane of glass. We are also investing deeply into building user experiences and workflows that help take Machine learning models from experimentation to production seamlessly. Come help us scale this team and fundamentally influence every key business decision made at Uber be a data driven decision.

What you’ll do

 

  • Attract, Hire, Mentor and Retain the best tech talent
  • Build and lead a diverse globally distributed team of 10+ engineers with a mix of Data science, Big data, Infrastructure, and full stack engineering backgrounds
  • Operate at the intersection of Data Infrastructure, Data science & Machine learning and explore all aspects of a Data science platform including hardware & compute infrastructures (GPU/TPU etc),  hardware-software codesign, ML algorithms & frameworks, in-house and 3rd party ML platforms and data visualization & dashboarding tools
  • Develop cross-org partnerships with peer organizations; collaborate and address their Data science, Machine learning & ad hoc analytics requirements
  • Influence and guide strategy, execution and innovation for all aspects of adhoc and experimental analytics at Uber
  • Engage with the open source community to understand existing work and influence future roadmap; Represent Uber via talks at conferences and blog posts

 

What you’ll need

 

  • 3+ years of management experience scaling and managing 5+ person teams with a track record of delivering results while growing/mentoring engineers on your team
  • Experience going through the full software cycle of requirements, design, coding/testing best practices and operational excellence in delivering world class software and services
  • Communication and leadership skills, with the ability to initiate and drive processes and projects proactively
  • Solid understanding of Data science, Machine learning, or compute & network infrastructures
  • Be customer obsessed and have the ability to translate customer and technical requirements into detailed engineering plans, architecture and design
  • Give technical feedback and drive quality via code reviews, design reviews and postmortems

 

Bonus points if

 

  • Under the hood experience with Machine Learning platforms such as Tensorflow, H2O.ai, Domino Data Lab etc is a strong plus
  • You have a strong vision for Data science tooling in a fast-paced environment like Uber while staying on top of rapid developments in the ML industry
  • Experience with highly available/fault tolerant, distributed systems, large scale data processing systems or enterprise/cloud analytics systems is also a strong plus