Data Scientist 75 views

Job Description

Don’t sit in meetings for a mission you don’t care about. Build Tools to Learn Faster at Grasp.

Company

The mission of Grasp is to increase the rate at which humans learn. A decade from now, Grasp will be able to take anyone from novice to mastery, in any field, ASAP. We will be the centre of learning.We’ve raised $4M+ from top tier investors including Balderton, Point9, and Mozilla! Our founders are Ed Matthews and Jacob Sidorov, who built Revolut’s trading desk together, and are both avid self-learners.Grasp is also a member of Makerversity, a pioneering community of over 350 world-leading entrepreneurs, creators and innovators.

Role

As the first full-time data scientist, you will take responsibility for our machine learning problem space. You will design and implement production solutions to problems, from scratch.Currently the majority of our ML problems are Statistical NLP problems. You must have experience in this field.It will be hard but rewarding work. Don’t apply if you want an easy ride.This is a hybrid office position requiring you to be in London regularly.

Requirements

Essential

• Bachelors/Masters/PhD in a STEM field (Mathematics, Computer Science, Engineering, Physics, Statistics or equivalent)
• Mastery of Python and SQL
• A proven track record applying Statistical NLP methods, especially deep learning and text-embedding based solutions, to solve real world problems
• Strong reason to believe that you can setup your own ML deployment system with the help of at most one software engineer
• A rigorous foundation in:
  • Probability Theory,
  • Information Theory,
  • Applied Probability & Statistics,
  • Deep Learning,
  • Reinforcement Learning,
  • Partial Observability
• Excellent communication skills
• Confident in a high-pressure environment
• You love learning and want to be able to do it faster

Nice to Have

• You have solved image/video information extraction problems
• You’re proficient with any other part of our technology stack: TypeScript, React, Docker/K8s DevOps
• You are experienced working with events-based systems
• You have worked at a fast-paced startup

Benefits

• Compensated: Strong base salary. Breakfast/dinner when at the office
• Invested: A mission you care about. A stock option plan designed for long term alignment
• Tools: New MBP or vintage Thinkpad ? Herman Miller chair or a bean bag ? Whatever you need to be productive
• In flow: Short feedback loops, ultra-aligned team, direct user contact