Role: Data Scientist
This role is part-funded by the ERDF
Transforming Healthcare through Predictive Analytics. With links to CERN and the University of Cambridge, we are tackling serious medical conditions.
Predictive analytics is the next frontier in medicine. Today, patients with chronic medical conditions suffer life-threatening disease complications without warning. Tomorrow, patients and healthcare providers will use Transformative tools to identify the subtle physiologic changes that precede symptom onset, enabling personalised preparation and prevention.
Using world-class AI and analysis techniques from CERN and Cambridge, we’ve developed an algorithm that predicts Sudden Cardiac Arrest (SCA). Annually, 6 million people die of SCA caused by ventricular arrhythmias. No available technology warns patients or healthcare providers of an oncoming arrest. We’re changing this.
In the first instance, your role will primarily involve working with the Transformative team on its ventricular tachyarrhythmia prediction algorithm as it is validated and further developed on the MIMIC-III database.
In the long term, your focus will be to create targeted models for well-defined conditions with highly detrimental acute outcomes. As a team, Transformative is developing artificially intelligent predictive monitoring services that avoid complications of a broad range of chronic medical conditions. Recently, very good results have been achieved in the prediction of septic shock with clear evidence of significant progression over time as the sophistication of the models is increased and further data are used as input.
The ultimate aim of our work will be to contribute to the transformation of the health system, ushering in an era of personalised prevention that creates better health, improves patient experience, and lowers the unnecessary costs of disease complications.
As an example of the potential impact of your work, sepsis is a leading cause of death in the United States, with mortality highest among patients who develop septic shock. 750,000 patients develop severe sepsis and septic shock in the United States each year. More than half of them are admitted to an ICU, accounting for 10% of all ICU admissions, 20 to 30% of hospital deaths, and $15.4 billion in annual health care costs. Several studies have demonstrated that morbidity, mortality, and length of stay are decreased when severe sepsis and septic shock are identified and treated early.
Your skillset should include some, or most, of the following:
- Ability to adapt quickly to evolving computing paradigms and requirements
- Dynamic languages: Matlab, Python
- Common machine learning environments: tensorflow, keras, scikit-learn
Nice to have:
- Expertise in ICU or CCU vital-sign monitoring data processing and analysis
- Compiled languages: C++
- PhD Degree
- Passion for our mission
- Ability to successfully lead projects and work remotely
We offer flexible start-up working and the opportunity to work with an exciting med-tech startup with an industry-changing product.
- Location: UK (Remote)
- Start Date: 1 August 2018
- Salary: Commensurate with experience
How to apply: Send your CV and covering letter to Lizzy@capitalenterprise.org with the reference “KEEP13” in the subject line
Issue date: Friday 29th June 2018
Closing date: Tuesday 31st July 2018
How to apply: Send your CV and covering letter to Lizzy@capitalenterprise.org with the reference “KEEP08” in the subject line
Issue date: Thursday 22nd March 2018
Closing date: Monday 30th April 2018