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- Tasks: Lead ML solutions for drug discovery using advanced techniques like graph neural networks.
- Company: Mission-driven tech company in life sciences, focused on innovative drug discovery.
- Benefits: Competitive salary up to £160,000, early equity, and comprehensive benefits.
- Other info: Collaborative environment with significant influence over technical direction and career growth.
- Why this job: Make a real impact in drug discovery while working remotely with cutting-edge technology.
- Qualifications: Expertise in ADMET or Structural Biology and strong ML engineering skills.
The predicted salary is between 160000 - 160000 £ per year.
The Client: A mission-driven technology company operating in the life sciences domain is seeking a Principal Scientist - hands-on with either ADMET or Structural Biology modelling, ML engineer to lead the technical direction for ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) modeling efforts within its drug discovery platform. The organisation enables collaborative model development across partner organisations while maintaining strict data privacy and ownership, using a federated data infrastructure.
In this hands-on, high-impact role, you’ll work at the intersection of machine learning, computational chemistry, and applied research to advance foundational model applications in drug discovery. You'll be the technical authority on ML architecture, experimentation, and strategy, while focusing specifically on data security and privacy. You will also collaborate closely with leadership and mentor other engineers and researchers. While this is not a people management position, it offers significant influence over technical direction.
Responsibilities:- Lead the design and implementation of ML solutions for ADMET using cutting-edge techniques such as graph neural networks and transformers.
- Lead the research and implementation of data privacy within the models and establish privacy attack-surface assessment.
- Develop and extend models for specific applications, including data distillation, benchmarking, and evaluation.
- Define preprocessing and harmonization strategies for diverse assay datasets used in ADMET modeling.
Principal Machine Learning Scientist | ADMET/Structural Biology | Series A - Drug discovery Platform | Fully Remote, EU | Base Salary , plus early equity+benefits in Chester employer: Owen Thomas | B Corp™
Contact Detail:
Owen Thomas | B Corp™ Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Principal Machine Learning Scientist | ADMET/Structural Biology | Series A - Drug discovery Platform | Fully Remote, EU | Base Salary , plus early equity+benefits in Chester
✨Tip Number 1
Network like a pro! Reach out to professionals in the life sciences and machine learning fields on LinkedIn. Join relevant groups and participate in discussions to get your name out there and show off your expertise.
✨Tip Number 2
Prepare for those interviews! Research common questions for Principal Machine Learning Scientist roles, especially around ADMET and Structural Biology. Practise articulating your experience with ML architecture and data privacy strategies.
✨Tip Number 3
Showcase your projects! Create a portfolio or GitHub repository that highlights your work with ML solutions, particularly in drug discovery. This will give potential employers a tangible sense of your skills and creativity.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of exciting opportunities, and applying directly can sometimes give you an edge. Plus, it’s a great way to stay updated on new roles that fit your profile.
We think you need these skills to ace Principal Machine Learning Scientist | ADMET/Structural Biology | Series A - Drug discovery Platform | Fully Remote, EU | Base Salary , plus early equity+benefits in Chester
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of Principal Machine Learning Scientist. Highlight your experience with ADMET or Structural Biology modelling, and don’t forget to showcase any hands-on projects that demonstrate your skills in ML architecture and data privacy.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about drug discovery and how your background aligns with our mission. Be sure to mention specific techniques like graph neural networks and transformers that you’ve worked with.
Showcase Your Collaborative Spirit: Since we value collaboration, make sure to highlight any experiences where you’ve worked closely with teams or partnered with other organisations. This will show us that you can thrive in a collaborative environment while maintaining data privacy.
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’re considered for this exciting opportunity. Plus, it’s super easy!
How to prepare for a job interview at Owen Thomas | B Corp™
✨Know Your Stuff
Make sure you brush up on your knowledge of ADMET and Structural Biology modelling. Be ready to discuss specific techniques like graph neural networks and transformers, as well as how they apply to drug discovery. The more you can demonstrate your expertise, the better!
✨Showcase Your Hands-On Experience
Since this role is hands-on, be prepared to share examples of your previous work in machine learning and computational chemistry. Talk about projects where you've led technical direction or implemented ML solutions, and highlight any challenges you overcame.
✨Emphasise Data Privacy
Given the focus on data security and privacy, make sure you can articulate your understanding of these concepts. Discuss any experience you have with privacy attack-surface assessments or implementing data privacy measures in your models.
✨Collaboration is Key
This role involves working closely with leadership and mentoring others, so be ready to talk about your collaborative experiences. Share how you've worked with cross-functional teams and contributed to a positive team environment, even if it's not a formal management position.