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- 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
- 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
- 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 employer: Biostream
Contact Detail:
Biostream Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Biostatistician – Bayesian Modelling
✨Tip Number 1
Network like a pro! Reach out to professionals in the biostatistics field, especially those with experience in Bayesian modelling. Attend relevant meetups or webinars to make connections and learn about hidden job opportunities.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your work with Bayesian models and probabilistic frameworks. This could be through GitHub or a personal website where you can demonstrate your programming prowess in Python or R.
✨Tip Number 3
Prepare for interviews by brushing up on real-world applications of your skills. Be ready to discuss how you've tackled noisy data or integrated statistical models into machine learning pipelines. We want to see your problem-solving skills in action!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Biostatistician – Bayesian Modelling
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 developing those statistical frameworks we’re after.
Showcase Your Programming Skills: Since strong programming skills in Python or R are key for this role, make sure to mention any relevant projects or experiences. If you've worked with tools like PyMC or Stan, give us the details – we love seeing practical applications of your skills!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re keen on joining the StudySmarter family!
How to prepare for a job interview at Biostream
✨Know Your Bayesian Basics
Make sure you brush up on your Bayesian modelling concepts before the interview. Be ready to discuss how you've applied these techniques in past projects, especially in relation to physiological data or medical risk. This will show that you not only understand the theory but can also apply it practically.
✨Showcase Your Programming Skills
Since strong programming skills in Python or R are crucial for this role, be prepared to talk about specific projects where you've used these languages. If possible, bring examples of code or models you've developed, especially those involving probabilistic frameworks like PyMC or Stan.
✨Prepare for Real-World Scenarios
The role involves working with noisy and incomplete data, so think of examples from your experience where you've tackled similar challenges. Discuss how you approached uncertainty quantification and what strategies you used to ensure robust decision-making under such conditions.
✨Collaborate and Communicate
This position requires collaboration with AI engineers and medical advisors, so be ready to demonstrate your teamwork skills. Share experiences where you've successfully worked in interdisciplinary teams, highlighting how you communicated complex statistical concepts to non-statisticians.