Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Develop Bayesian models to assess medical risk and support triage prioritisation.
- Company: Innovative health tech company focused on real-world medical applications.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Why this job: Make a real difference in healthcare by using data to save lives.
- Qualifications: PhD or strong MSc in relevant fields and experience with Bayesian modelling.
- Other info: Collaborate with experts in AI and medicine in a dynamic environment.
The predicted salary is between 50000 - 70000 £ per year.
We are looking for a biostatistician with expertise in Bayesian modelling to work on probabilistic modelling of physiological data and medical risk. The role focuses on developing statistical frameworks that combine multiple data sources to estimate the probability of life-threatening deterioration and support triage prioritisation. You will work closely with AI engineers, medical advisors, and the product team to design models that operate in real-world environments with noisy and incomplete data.
Responsibilities
- Develop Bayesian statistical models to estimate physiological deterioration risk from multi-sensor data
- Build probabilistic frameworks integrating vital signs, haemodynamic indicators, motion data, and environmental variables
- Design and run simulation studies to test triage algorithms and risk scoring systems
- Work on uncertainty quantification and probabilistic inference for medical decision support
- Collaborate with engineers to integrate statistical models into machine learning pipelines
- Analyse physiological and operational datasets to identify predictive patterns
- Support validation of models against real-world medical outcomes
- Contribute to scientific publications and technical documentation
Profile
- PhD or strong MSc in biostatistics, epidemiology, statistics, applied mathematics, or a related field
- Experience with Bayesian inference and probabilistic modelling
- Familiarity with health data, physiological signals, or medical datasets
- Strong programming skills in Python or R
- Experience with probabilistic frameworks such as PyMC, Stan, TensorFlow Probability, or similar
- Ability to work with noisy or incomplete real-world data
Nice to Have
- Experience with physiological monitoring, biosensors, or wearable devices
- Experience with causal inference or survival analysis
- Experience working with clinical datasets or emergency medicine
- Interest in defence technology, emergency response, or austere medical environments
Biostatistician – Bayesian Modelling in London employer: Biostream
Contact Detail:
Biostream Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Biostatistician – Bayesian Modelling in London
✨Tip Number 1
Network like a pro! Reach out to professionals in the biostatistics field, especially those who work with Bayesian modelling. Attend relevant meetups or webinars, and don’t be shy to slide into their DMs on LinkedIn. You never know who might have a lead on your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your work with Bayesian models and probabilistic frameworks. Include any projects where you’ve tackled real-world data challenges. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Be ready to discuss how you’ve handled noisy data or collaborated with engineers in the past. Practise explaining complex concepts in simple terms – it’ll impress the interviewers!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Keep an eye on our job postings and make sure your application stands out by tailoring it to the specific role and highlighting your relevant experience.
We think you need these skills to ace Biostatistician – Bayesian Modelling in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Bayesian modelling and any relevant projects. We want to see how your skills align with the role, so don’t be shy about showcasing your expertise in probabilistic frameworks!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about biostatistics and how your background makes you a perfect fit for our team. Let us know how you can contribute to our mission!
Showcase Your Technical Skills: We’re keen on your programming skills, especially in Python or R. Be sure to mention any specific projects or experiences where you’ve used these languages to develop statistical models or analyse data.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy – just follow the prompts!
How to prepare for a job interview at Biostream
✨Know Your Bayesian Basics
Make sure you brush up on your Bayesian modelling concepts. Be ready to discuss how you've applied these techniques in past projects, especially in relation to physiological data. This will show your depth of knowledge and practical experience.
✨Showcase Your Programming Skills
Since strong programming skills in Python or R are crucial for this role, prepare to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice coding exercises related to statistical modelling and data analysis.
✨Familiarise with Real-World Data Challenges
Understand the common issues that arise when working with noisy or incomplete data. Be prepared to discuss strategies you've used to handle such challenges in previous roles, as this will highlight your problem-solving skills and adaptability.
✨Collaborate and Communicate
This role involves working closely with AI engineers and medical advisors, so emphasise your teamwork and communication skills. Prepare examples of how you've successfully collaborated on interdisciplinary projects, as this will demonstrate your ability to work effectively in a team environment.