Company:

Research Grid (R.grid) is an early-stage start-up that uses machine learning to streamline administrative medical research processes. Our novel system, R.grid, includes high-volume automated document population, real-time qualitative data collection, intelligent reports and analyses, as well as communication and recruitment support.

We are a small but growing team of data scientists, engineers and business professionals passionate about improving the clinical research landscape, helping researchers remove the arduous, time-consuming, manual processes from their workflows.

Job Overview:

We are looking for a Senior Natural Language Processing Engineer to lead our Machine Learning team in the development of new features. This senior role will work closely with the CEO and other team members in the development of great features for our customers.

You should have strong python skills, understand how to test code for commercial production, problem-solving ability and a background in mathematical modelling and statistical analysis. You should also be able to demonstrate a strong track record of using your NLP expertise in a commercial environment.

You should be self-motivated, consistent, disciplined, able to deliver to deadlines and confident leading teams. If you’re also able to align our products with our business goals, we’d like to meet you.

Salary: £Competitive

Seniority Level: Senior

Responsibilities and Duties:

  • Build and expand NLP algorithms, analytic systems, and other predictive models
  • Plan and manage data projects
  • Collaborate with a team of software engineers and data scientists
  • Data mining and collection procedures
  • Ensure data quality and integrity
  • Interpret and analyse data problems
  • Conceive, plan and prioritise data projects
  • Test performance of data-driven products
  • Visualise data and create reports
  • Experiment with new models and techniques
  • Align data projects with organisational goals

About our recruitment process:

We have a 2 stage interview process, with an NLP task to be completed ahead of the first stage interview, at which time the solution to the task will be discussed and reviewed.