Remote Data Science Jobs in Web3

679 jobs found

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Job Position Company Posted Location Salary Tags

Braintrust

San Francisco, CA, United States

$30k - $67k

Braintrust

San Francisco, CA, United States

$91k - $156k

SD Solutions

Remote

$45k - $75k

Keystone

remote

Kronos Research

remote

$63k - $80k

OpenSea

Remote

$45k - $75k

Worldcoin

remote

$45k - $60k

QuickNode

Remote

$105k - $111k

Palm NFT Studio, Inc.

New York, NY, United States

$72k - $100k

Mandana.Impact

Remote

Immutable

Australia

$90k - $100k

Virtually Human Studio

United States

$105k - $111k

SwissBorg

Europe

$45k - $75k

TipTop

Remote

$165k - $260k

Injective Labs

Remote

$32k - $76k

Data Engineer US [Remote]

Braintrust
$30k - $67k estimated

This job is closed

  • JOB TYPE: Freelance, Contract Position (no agencies/C2C - see notes below)
  • LOCATION: US ONLY EST | Partial overlap
  • HOURLY RANGE: Our client is looking to pay $88 - 107$/h
  • ESTIMATED DURATION: 40h/week - long-term, ongoing project

THE OPPORTUNITY

Requirements

WHAT YOU'LL DO:

Design, build and maintain data pipelines that power audience visualization platform
Maintain system architecture documentation and runbooks
Build, automate, maintain, scale, and monitor user-facing systems using standard methodologies, with reliability and scalability in mind
Collaborate with other specialists, product managers, and designers to identify and solve exciting problems, crafting an awesome engineering experience.
We are a distributed workforce enabling our band members to find a work mode that is best for them! For this role, it can be within the Americas region in which we have a work location.

WHO YOU ARE:

  • You know how to work with data pipelines and have experience working with technologies such as Spark or Beam
  • You have experience with Java, Scala and their tooling ecosystem
  • You are experienced with deploying and operating services on Linux
  • You care about quality and you know what it means to ship high quality code
  • You have experience with large scale data systems