Data Scientist

Employer
1715 Labs
Location
Cranford, UK
Salary
Competitive
Closing date
14 Oct 2020

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Sector
Technology & New Media
Contract Type
Permanent
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Location: London or Oxford, UK

Salary range: £40-50k + equity options

Want to be part of a team that's transforming the future of supervised learning? 1715 Labs is a venture backed, seed stage commercial spinout from the world-leading citizen science platform, Zooniverse.org. We are looking for a data scientist to join our product team to help build a world class labelling platform.

We already have a broad range of customers, from large FTSE50 corporates to cutting edge start-ups with their own deep tech applications. We'll need you to be adaptable, creative, and driven to meet our customer's challenging performance metrics and timelines.

Some of the cool stuff about the role:
  • as one of the first team members you'll get to shape the future product - your opinion will be sought after and valued;
  • you'll get to work across a broad range of industries, sectors, and settings;
  • significant exposure to customers - we'll need you to engage directly with customers to deliver a product that exceeds their expectations;
  • you'll also have support from an established team at the Zooniverse, so you won't be on your own;
  • you'll have the opportunity to get stuck into additional areas that interest you from design and even marketing;
  • lots of opportunities to be innovative and creative, to develop methods that can solve a wide range of challenges.


Technical skills we would like you to bring:
  • strong numeric python skills (Jupyter, pandas, Dask);
  • competent at data visualisation (matplotlib, seaborn, bokeh);
  • experienced with the use of REST APIs (and GraphQL ideally) to request data / interact with services;
  • experience setting up and using cloud infrastructure on Google Cloud, AWS, or Azure (or a desire to learn how to do such things);
  • understanding of basic NN architectures for CV / NLP problems;
  • understanding of basic ML tasks (classification vs regression);
  • how to detect overfitting and bias variance trade-off;
  • understanding of bayesian statistics, ideally with experience in probabilistic programming;
  • familiarity with tensorflow or pytorch;
  • understand how to use transfer learning to adapt an existing model to a new problem.
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