NLP Data Science

IOMED Published: July 2, 2020
Barcelona, Spain
Job Type


About the position

The work consists essentially of designing, implementing and training algorithms that extract information from the clinical text, as well as collaborating in the development and maintenance of the main products of our company. We don't draw a hard line between our research and engineering teams: they both research and develop market-quality software, putting previous research into practice. We mainly work in Python, using our own and existing NLP libraries.

About you

You have skills in text mining or other non-trivial NLP tasks, and you feel comfortable programming seriously (you must have Git skills, experience with libraries like pandas, scikit, tensorflow, and know how to create reusable and extensible code). You like teamwork, teaching and learning from others, and talking about what you're working on, with both technical and non-technical team members. In addition, you can prepare scientific communications about the work he has done.

What are we look for

  • Knowledge of Python and useful libraries for data science (pandas, numpy, sklearn, etc.)
  • General knowledge of Machine Learning.
  • Experience implementing neural networks (NN) and creating new NN models, hyperparameter adjustment, etc. Experience with a NN library (Keras / Tensorflow / theano / others).
  • Experience implementing deep learning models.
  • Experience in Natural Language Processing.


It is important that you feel comfortable helping the rest of the team to get the job done. However, the position is definitely about:

  • Creation and testing of NLP models oriented to medical text processing. The tasks are, among others:
    • Named Entity Recognition
    • Named Entity Linking
    • Word-sense disambiguation
    • Language Modeling
    • Text Classification
    • Relation Extraction
  • Collaborate in writing papers and posters.
  • Adaptation of the models to production environments.

What we offer

  • Negotiable salary.
  • Indefinite full-time contract.
  • Company profit sharing program.
  • Flexible hours, with the possibility of a remote day a week.
  • A warm, transparent and supportive team that maintains a balance between work and personal life.
  • Most days, we have lunch together on our sunny terrace.


IOMED is a technological company of software development. It was launched in 2016, funded by local and international ventures. We are passionate and talented young professionals, from all around Spain and the world (It couldn't be any other way, as we're based in beautiful and bright Barcelona). Our "dream team" is made up by mathematicians, statisticians, bioinformaticians and physicians.

As a startup, we are looking for people who are eager to innovate and be part of a project with impact on healthcare industry, enjoying what we do, team-work and taking on new challenges. IOMED is an equal opportunity employer. We are still a small team and are committed to growing in an inclusive manner. We want to augment our team with talented, dynamic people irrespective of race, color, religion, national origin, sex, physical or mental disability, or age.

What we do

Nowadays, around 50% of Clinical Trials are delayed due to patient recruitment, since patient data collection is performed in a manual fashion. As a result, clinical research is highly inefficient both in time and cost, taking years and billions of dollars to develop a new drug. This problem could be solved through Real World Data, i.e. derived from Electronic health records (EHR). But unfortunately up to 85% of existing clinical data is unstructured, i.e. in plain text. This also leads, in part, to the existence of data silos, making it impossible to aggregate data from different hospitals.

IOMED has found the solution to this situation, making it possible to take advantage of full value of clinical Real World Data. We developed a tool that extracts the necessary data from clinical texts, which results in a structured, standardized and interoperable database that contains the complete clinical information from hospitals. By this means, non-reusable information is transformed into data available for Clinical Research, allowing an enormous increase in criteria- compliant patients and a reduction of total time and manual labor devoted to this task.

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