NLP Engineer

AstraZeneca is looking for a skilled NLP Engineer/Scientist to join our NLP-team within AI Engineering.



Our global team is working in collaboration with our scientists to help develop better drugs faster, choose the right treatment for a patient and run safer clinical trials. We empower our scientists from early development to the late stages in drug development, driving innovation and acting as a catalyst for the adoption of the latest advances in Artificial Intelligence and Data Science. As such, we work closely with scientists & product teams and deliver NLP solutions at scale within the AstraZeneca tech stack, whilst encouraging best practices for NLP across the company.



As the ideal candidate, you will possess a blend of NLP research skills, engineering skills, and successful experience building quality applications related to NLP and machine learning in a scientific environment. You will help envision, build, deploy and develop our next generation of NLP models and tools at scale.



Key Accountabilities:

- Latest NLP tooling and models

- Research and apply new algorithms and methods to relevant business problems

- Understanding & application of fine-tuning language models

- Work closely with data scientists, machine learning, engineering and platform teams

- Help other teams to access and leverage natural language to answer their research- and business-problems



Candidate Knowledge, Skills and Experience:

- Masters / PhD in Machine Learning, Computer Science, Mathematics or similar field with 3+ years of experience specializing in natural language processing or deep learning

- Strong coding and software engineering skills such as Python, Java,...

- Experience in using unsupervised and supervised methods over unstructured data: especially with search, text analytics and NLP

- Experience building large scale data processing pipelines

- Working knowledge of cloud environment, dockers, etc.

- Knowledge of scraping documents and document extraction beneficial

- Ability to explain complex methods and techniques to a non-technical audience