Albus Health is a medical technology startup that develops intelligent remote monitoring systems for in-home patients taking part in clinical trials. We are looking for a Data scientist to join us in our mission to improve lives of a billion people worldwide struggling with chronic respiratory conditions such as asthma and COPD.

Our potent combination of technical, clinical and commercial experts has allowed us to gain significant commercial traction in a relatively short duration. Our solution is now in use by some of the world’s largest pharma companies and we are preparing to scale up our operations domestically and overseas.

Albus have partnerships with top hospitals in the UK and some of Europe’s best clinicians. We have been running multiple carefully selected clinical studies and trials for several years, and as a result, acquired a wealth of data. This data will be instrumental to further advance our technology and to continue extracting valuable insights that would prevent emergencies and deaths for people struggling with chronic conditions.

Albus spun out from the Department of Engineering and Respiratory Medicine at Oxford University back in 2017 and have since then won multiple wards (including AI in Health and Care Award by UK’s Health Secretary and UK Research and Innovation). Albus is firmly backed with multi million pounds investment and we are now looking to expand our technical team in Oxford to develop new products and solutions that can operate at a global scale.



The acoustics research scientist position is an R&D hands on role with the opportunity to mentor some more junior colleagues in some cases. The role will require an in-depth understanding of the acoustic signals produced by humans in a domestic environment. You will be responsible for the development of suitable signal processing algorithms to extract acoustic features and biomarkers for primary data analysis as well as downstream analysis processes. You will develop algorithms/models applying suitable machine learning techniques for a variety of tasks including classification and prediction. You will be asked to validate the output produced by your algorithm against reference data when available or through other means when not. 

​This role will be reporting directly to the CTO. You will also have access to a range of clinical and industry experts to gain insights, jointly steer research and identify novel solutions with high health impact and commercial scalability. After early adoption, Albus is now entering an exciting rapid growth phase that presents unique opportunities for career growth and progression, both technical and managerial. 

One of the most important areas of ownership will be the acoustic data captured. Given the volume of data and processing involved, software needs to be architected for efficiency and scale.  



In the first week, we will help you get a good overview of the company, its origins, current state and where we are headed. You will learn about customer requirements, the technology and the team.       

During the first month, you will understand the various technical challenges in more detail and familiarise yourself with our current code and algorithm, being able to make some small modifications of your own.

By the end of your first quarter you would have been able to undertake a sizeable work package/s addressing some of the new features in our roadmap.   



  • Engage with our clinical and commercial teams to understand deeply the clinical needs and requirements. Convert clinical needs into engineering problems. Solutions may involve combination of methods founded in electrical engineering, physics and computer science
  • Design and develop suitable signal processing algorithms to extract useful information and actionable insight
  • Formulate hypothesis for model improvement/optimisation, test/validate hypothesis and optimise input data and chosen model 
  • Design and implement experiments to test hypotheses, measure performance, identify sources of error and limitations in hardware and algorithms     
  • Sift and analyze data from multiple angles to explore opportunities 



  • MSc or PhD in electrical engineering, DSP, information engineering, biomedical engineering, physics or related fields
  • 2-5 years commercial experience working on digital signal processing of acoustic signals
  • Experience with frequency content & spectrum analysis and solid understanding of the different sources of noise, signal degradation and techniques to extract meaning from raw noisy signals
  • Previous experience with classical ML models such as SVM, random forest, XGB boost as well as Deep Learning models
  • Experience creating deep learning models using at least one of the following frameworks: TensorFlow, PyTorch, Keras
  • Track record of high quality publications or inventions. Comfortable with literature review and implementation of state of the art published research
  • High proficiency in Python programming and ideally also in Matlab and C++. Experience writing production code



  • Experience working with a healthcare or medical technology start-up will be a distinct advantage, specially with remote symptom monitoring products
  • Knowledge or experience with time series analysis     
  • Experience in developing analysis models for a range of computing capabilities, from microprocessors to multi-core GPUs
  • Practical know how on using cloud computing (AWS, Azure or GCP) to store and process data at scale as well as data management systems (e.g. MySQL or Data Marts or Data Warehouse)    
  • Previous experience with Linux OS and containerised architectures (Docker or Kubernetes, etc.) 
  • Productisation experience for solutions with a significant machine learning, signal processing and electrical engineering components, encompassing cloud and hardware sub-systems. In particular in a regulated industry such as healthcare



  • Competitive salary (based on experience) 
  • Comprehensive benefits package in addition to base salary 
  • You will be offered equity in the company through EMI scheme 
  • Possibility of flexible working hours and remote working arrangements
  • Regular company socials and activities