Lead / Senior Machine Learning Engineer

Lead / Senior Machine Learning Engineer

About Us:

At Sensyne Health we combine technology and ethically sourced patient data to help people everywhere get better care. To do this, we have created a unique partnership with the NHS that delivers a return to our partner Trusts and unlocks the value of clinical data for research while safeguarding patient privacy. Alongside this, we develop clinically validated software applications that create clinician and patient benefit while providing highly curated data. Our products include vital-signs monitoring in hospitals and patient-to-clinician apps to support self-care and remote monitoring of gestational diabetes and chronic diseases such as COPD and heart failure.

We use our proprietary clinical AI technology to analyse ethically sourced, clinically curated, anonymised patient data to solve serious unmet medical needs across a wide range of therapeutic areas, enabling a new approach to clinical trial design, drug discovery, development, and post-marketing surveillance.

The Team:

We are an agile team of clinically-led data scientists, machine learning researchers and engineers who use our proprietary clinical AI technology to analyse ethically sourced, clinically curated, anonymised patient data to solve serious unmet medical needs across a wide range of therapeutic areas, enabling a new approach to clinical trial design, drug discovery, development and post-marketing surveillance. The nature of our team is collaborative with an emphasis on genuine passion for healthcare.


The Role:

As a leading member of a dynamic, multi-disciplinary team of scientists passionate about reinventing the way new medicines are developed, you will drive the next generation of innovation for better patient outcomes, whilst harnessing some of the industry’s most progressive AI approaches. The role is about being the technical link between our inhouse research and product/engineering teams centred around productionising our machine learning algorithms, developing scalable pipelines and investigating data-efficient ways to use our real-world patient data.

This role will be reporting into our Head of ML Research, with whom you will work closely to lead the design and selection of analytical tools and state-of-the art data integration techniques that ensure we uphold our strict ethical policies, while releasing the clinical insights in the real-world data.

Your work with experts in artificial intelligence, deep learning, informatics, statistics, drug discovery and clinical translation/commercialisation as well as software engineering will enable us to optimise our analytical approaches and accelerate the transition processes from early prototype to a real product.

Responsibilities:

  • Building and implementing Data Science and ML solutions to provide actionable intelligence to support business decision-making within life sciences.
  • Help develop robust model training and data infrastructure to support continuous optimisation of ML-driven approaches.
  • Support and guide other researchers on best practices in programming through all stages of project development (e.g. unit testing, OO design, documentation).
  • Being the technical link between our inhouse research and product teams centred around productionisation of our machine learning algorithms, developing scalable pipelines and investigating data-efficient ways to use our real-world patient data.


Requirements:

  • Higher university degree (e.g. MSc) in Computer Science, Engineering, Mathematics, Physics with focus on AI, ML or data science.
  • Practical experience with ML in research and development projects
  • Experience in developing scalable ML pipelines (incl. feature extraction, model optimisation and evaluation, documentation etc.)
  • Experience building data solutions incrementally, integrating and managing datasets from multiple sources.
  • Solid programming experience in Python, however significant experience in other high-level languages is beneficial (i.e. C/C++/C#/Java/Rust)
  • Hands-on experience using one of the following data science libraries: Pandas, Numpy, Sklearn, Tensorflow, PyTorch or similar.
  • Version Control (e.g. git)
  • Experience with Azure and Azure ML (e.g. pipelines) or similar
Desirable:
  • Experience of analysing clinical/healthcare data is a bonus
  • Good understand of classical and deep learning based machine learning methods such as random survival forest or autoencoders
  • Experience working with and building relational databases in SQL
  • Continuous Integration


  • 5% employer matched salary sacrifice Pension scheme
  • Life Assurance & Income protection
  • A range of health, wealth and lifestyle benefit plans including BUPA, Gym and holiday trade options
  • Electric Vehicles & Cycle to work schemes
  • Proactive career development planning
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