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- Tasks: Develop and evaluate predictive models linking viral genotype and phenotype data using machine learning.
- Company: Join a large global organisation focused on impactful bioinformatics research.
- Benefits: Remote work, competitive contract rates, and potential for extension.
- Why this job: Make a real difference in healthcare by applying your ML skills to biological datasets.
- Qualifications: Strong background in machine learning, Python proficiency, and experience with biological datasets.
- Other info: Collaborative environment with opportunities for professional growth and innovation.
The predicted salary is between 60000 - 80000 £ per year.
A large global organization are seeking a Senior Machine Learning Scientist to develop and evaluate internal predictive models linking viral genotype and phenotype data based sensitivity analyses. The role focuses on applying established machine‑learning approaches to curated biological datasets to support research, validation, and internal decision‑making. This role will be an initial 6-8 months+ contract with the possibility of extensions. The role can be worked on a remote basis.
Key Skills/Responsibilities:
- Technical Expertise
- Strong background in machine learning or statistical learning with substantial hands‑on experience developing classification models
- Experience working with high‑dimensional, sparse biological or omics datasets
- Strong proficiency in Python for end‑to‑end machine‑learning workflows
- Demonstrated experience designing validation strategies and assessing performance under significant class imbalance and limited sample sizes
- Scientific Rigor
- Clear understanding of model limitations, uncertainty, and overfitting risks in real‑world biological datasets
- Experience delivering machine‑learning analyses intended to inform research and internal decision‑making
- Experience making principled methodological recommendations in the face of incomplete or noisy data
- Preferred Qualifications
- Experience working with biological sequence data or genotype–phenotype analyses
- Experience with interpretability or explainability approaches applied to biological machine‑learning models
- Background in pharmaceutical, biotech, or regulated research environments
- Machine Learning Model Development
- Develop classification models to analyze curated genotype–phenotype datasets
- Apply appropriate modeling strategies to predict viral sensitivity or resistance based on sequence‑derived features
- Implement training, validation, and hyperparameter‑tuning workflows using predefined datasets
- Evaluate alternative feature representations provided by the bioinformatics team and assess their suitability
- Model Evaluation and Robustness
- Assess model performance using metrics appropriate for imbalanced biological datasets
- Evaluate robustness across data splits, phenotype definitions, and successive data releases
- Identify failure modes, instability, and limitations, and document their implications
- Document modeling assumptions, trade‑offs, uncertainty, and limitations in a reproducible and transparent manner
- Interpretability and Insight Generation
- Provide interpretable summaries of model behavior, including feature importance and consistency of signals
- Identify amino‑acid positions or features that recur across models or resampling strategies, while highlighting where signals are not reproducible
- Clearly document and communicate findings, assumptions, and caveats within the bioinformatics team
Senior Machine Learning Scientist – Bioinformatics, Python, ML – UK, Remote in Sheffield employer: MRP-Global
Contact Detail:
MRP-Global Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Scientist – Bioinformatics, Python, ML – UK, Remote in Sheffield
✨Tip Number 1
Network like a pro! Reach out to your connections in the bioinformatics and machine learning fields. Attend virtual meetups or webinars, and don’t be shy about asking for introductions. We all know that sometimes it’s not just what you know, but who you know!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those related to biological datasets. Use platforms like GitHub to share your code and results. This gives potential employers a taste of what you can do, and we love seeing your work!
✨Tip Number 3
Prepare for interviews by brushing up on common machine learning concepts and techniques. Be ready to discuss your experience with classification models and how you’ve tackled challenges like class imbalance. We want to see your thought process and 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’re always looking for passionate candidates who are eager to make an impact in the field of bioinformatics and machine learning.
We think you need these skills to ace Senior Machine Learning Scientist – Bioinformatics, Python, ML – UK, Remote in Sheffield
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your technical expertise in machine learning and Python. We want to see your hands-on experience with classification models and how you've tackled high-dimensional biological datasets. Don’t hold back on showcasing your achievements!
Be Clear and Concise: When writing your application, clarity is key! We appreciate straightforward language that gets to the point. Avoid jargon unless it’s necessary, and make sure your passion for bioinformatics shines through.
Tailor Your Application: Take a moment to customise your application for this role. We’re looking for specific experiences related to model evaluation and robustness in biological datasets. Show us how your background aligns with what we need!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Plus, it’s super easy!
How to prepare for a job interview at MRP-Global
✨Know Your Models Inside Out
Make sure you can discuss your experience with classification models in detail. Be prepared to explain how you've developed and validated these models, especially in the context of high-dimensional biological datasets. This will show that you understand the nuances of machine learning in bioinformatics.
✨Showcase Your Python Proficiency
Since Python is crucial for this role, brush up on your end-to-end machine learning workflows. Be ready to talk about specific libraries you've used, like scikit-learn or TensorFlow, and any unique challenges you've faced while implementing them in your projects.
✨Discuss Scientific Rigor
Highlight your understanding of model limitations and overfitting risks. Prepare examples where you've had to make methodological recommendations based on incomplete data. This demonstrates your ability to think critically and apply scientific principles in your work.
✨Communicate Clearly and Effectively
Practice explaining complex concepts in a straightforward manner. You may need to summarise model behaviour or interpret results for non-experts, so being able to communicate your findings clearly is key. Use examples from your past work to illustrate your points.