Our client is a early-stage biotech company in the heart of England that are providing people with the chance to detect diseases and illness as early as possible in their own homes by combining world-class design with equal science and technology by building tools that can be used by anyone. The devices are highly accurate and easy to use but also maintain the privacy that anyone world search for in their own home.

The role

• Develop & deploy data analysis tools that wider teams within the company can use to precisely and effectively process sample data which is collected by the products.
• Create in depth and easy to understand visualisation reports to help the team when reporting and presenting the data to both technical and non-technical stakeholders.
• Focus on the development of multivariable predictive models and evaluate their accuracy and further implementing changes where necessary.
• Stay up to date with the latest advancements in data science, machine learning, and healthcare technologies, and contribute to the exploration of new methodologies and techniques to enhance our diagnostic products.

Requirements: 

• A minimum of 5 years professional experience as a Data Scientist within the Biotech/Life Sciences sector.
• Masters or Doctoral level degree in a relevant field, which can include rare diseases, healthcare data science, statistics or public health.
• Strong experience working with large data sets and proficiency in data manipulation, cleaning and processing techniques.
• Proven track record of developing and evaluating predictive models (experience in deployment is a bonus).
• Proficiency in R, with the ability to write efficient and maintainable code and a keen interest in code review and good coding practice. Additional languages such as Go would be a plus, or previous experience with coding in programs such as Stata
• Full professional proficiency in English.

Further qualifications:

• Solid understanding of survival analysis, competing risks, cohort studies, statistical and machine learning approaches to multivariable modelling, and model validation.
• An understanding of IP and medical device regulations would be a plus.
• Attention to detail and commitment to producing accurate, interpretable, useful results.
• Solid understanding of statistical machine learning algorithms and experience applying them to real-world problems.