Senior Machine Learning Researcher NLP- Arcturis Data Oxford•Hybrid remote £75,000 – £85,000 a year – Full-time

Full time @Data Science Career in Machine Learning
  • Apply Before : March 22, 2024
  • Salary: £75.00 - £85.00 / Annual
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Job Detail

  • Job ID 21923
  • Sector Machine Learning

Job Description

The Role:

We have an open position for a skilled Senior ML Researcher with a strong background in natural language processing (NLP) to support and strengthen our ML Analytics team.

Senior ML Researcher will develop novel NLP models to analyse Electronic Healthcare Records (EHR) to improve disease understanding through e.g., the enrichment of real-world datasets and the identification of novel disease pheno- and/or endotypes across different disease areas. The role requires hands-on experience in processing and analysing of unstructured reports for tasks such as named entity recognition and a familiarity with state-of-the-art NLP techniques as well as their application in healthcare. Ideal candidate will have previous experience working with structured healthcare data to support ML-driven patient stratification and predictive modelling projects.

Senior ML Researcher will also guide the research direction in the field of NLP within Arcturis and support the delivery of novel insights to the clinical questions of our life sciences and healthcare partners.

The role will work closely with other researchers, the engineering team and informaticians to harness real-world data and analytics to push the boundaries of current disease understanding and to accelerate the development of novel therapies.

 


Key Responsibilities:

  • Collaborate in an interdisciplinary team to analyse unstructured and structured EHR and provide accurate, timely, and actionable insights across different disease areas.
  • Guide and develop robust and scalable NLP solutions to process unstructured clinical reports from multiple sources across different disease areas.
  • Prepare independently project proposals and analysis reports to address key clinical questions in collaboration with life sciences and healthcare partners.
  • Assist in steering the research direction in the field of clinical natural language processing and electronic health care data analysis.
  • Communicate results effectively to colleagues and external partners in the form of presentations, reports, and publications, including in scientific journals and at conferences.

Requirements:

  • PhD degree in Computer Science, Machine Learning, or closely related field.
  • Demonstrable hands-on experience in developing and deploying NLP ML solutions ideally within the healthcare context.
  • Excellent coding skills in Python and experience with common data science libraries (e.g., Tensorflow, PyTorch, Scikit-learn, Transformers, spaCy, NLTK).
  • Deep understanding of key data processing and state-of-the-art analytical methods (e.g., classical and deep learning based ML methods, transformer models, unsupervised and supervised learning techniques, clustering approaches, dimensionality reduction methods).
  • Ability to assimilate current literature in a therapeutic area and independently perform studies accordingly.
  • Capability to work and communicate effectively in an interdisciplinary work environment.
  • Excellent cross-disciplinary problem solving, time management and organizational skills.

 

Desirable:

  • Experience with cloud computing (e.g. Azure), joint code development (e.g. Github) and database systems (e.g. SQL/Postgres).
  • Experience working with structured and unstructured real-world patient data.
  • Previous experience in the healthcare industry.
  • Working experience in an interdisciplinary agile team.

 

What’s in it for you….

If you would like to find out more, please visit our Careers page: https://www.arcturisdata.com/careers/

 

  • If you are a not UK national or do not have the right to work in the UK, then you will need permission to work on the territory of the UK. If you are not sure what permissions are required, then you should seek advice before applying for the role.

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