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NPL Data Scientist in Barcelona

IOMED Medical Solutions

Workplace
Onsite
Hours
Full-Time
Internship
No
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Job Description

About the job

The core of your role is designing, implementing, and training algorithms that extract value from clinical data as well as to collaborate 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: you’ll do research and ship commercial quality software that puts it to use. We mostly work in Python, but dip into other languages when it makes sense to, so you’ll need to be comfortable picking up from a range of tools :

Data processing pipelines
Data model transformations
Components of a multi-modal distributed database.

About you

You have proven skills of putting microservices to use, and you’re comfortable with the containers in a cluster architecture. You work well in a team, can teach & learn from others, and communicate what you’re working on with non-technical team members.

Things You Might Do

IOMED is a startup, so you’ll have to be comfortable rolling up your sleeves and doing whatever needs doing. However, you can definitely expect to:

Build & evaluate ML-based algorithms for NLP tasks in clinical datasets
Hyper-parametrization and cross-validation of your models
Deploy and tweak the models to make them work in a production environment.
Testing, logging, metrics and alerting of the deployed services.
Collaborate on the writing of papers and posters

Things we expect

General knowledge of NLP and its tasks
Knowledge of Python, NumPy, Pandas, and a library of plots.
Machine learning theory, as well as bayesian statistics.
Training models on GPUs
Experience with a library of NN: Keras / Tensorflow / Pytorch/ others

What we offer

Gross annual wage: 30.000€-35.000€.
Full-time permanent contract
Profit-sharing scheme.
Flexi-time schedule, with possibility of home office once a week.
A warm, transparent and supportive team, with huge emphasis on work-life balance.
Most days, lunch together in our sunny terrace.

About IOMED

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

In the 322 million clinical consultations carried out in Spain every year a large amount of data is collected in a free text format (up to 85% in Catalonia). This leads to enormous inefficiencies both in time and cost for Clinical Research.

IOMED ́s work is to extract value from this real world data collected during medical practice, which was, until now, inaccessible. Our product unleashes data potential, enabling researchers and pharma companies to easily make analytics and ML models on complex data, having a direct impact on healthcare.

The tools we develop structure clinical texts written by physicians, extracting and encoding relevant medical concepts like symptoms and diseases, taking into account their context (negations, family/personal background, past events).

Backed-up up by our innovative developments, IOMED envisions to become the worldwide reference for Data Access in Clinical Research.


 
  • Health Tech

  • Barcelona, Spain

  • 2-10

  • 2016

IOMED has developed a Natural Language Processing tool that allows hospitals to structure their information identifying clinically relevant variables from medical notes and structuring them into datasets, which can then be easily queried. Thanks to this data structuration, professionals and researchers from the hospitals can access to the information stored in their facilities more quickly thus accelerating patient recruitment by reducing the time invested in this process. At the same time, it allows hospitals to improve the quality of their services.

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