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- Tasks: Lead ML solutions for drug discovery, focusing on ADMET modelling and data privacy.
- Company: Mission-driven tech company in life sciences, fully remote work culture.
- Benefits: Competitive salary up to £160,000, early equity, and comprehensive benefits.
- Other info: Influential role with significant technical direction and career growth opportunities.
- Why this job: Make a real impact in drug discovery using cutting-edge machine learning techniques.
- Qualifications: Expertise in ADMET or Structural Biology and strong ML engineering skills.
The predicted salary is between 160000 - 160000 £ per year.
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 Maidstone 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 Maidstone
✨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 match your skills.
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 Maidstone
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, Structural Biology, and any relevant machine learning projects. We want to see how your skills align with our mission!
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 makes you the perfect fit for this role. Let us know what excites you about working with us at StudySmarter.
Showcase Your Technical Skills: Don’t forget to highlight your technical expertise in ML architecture and data privacy. We’re looking for someone who can lead the charge in these areas, so make sure we see your hands-on experience and innovative thinking!
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, we love seeing applications come in through our own platform!
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. 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 ML architecture and experimentation. Talk about any projects where you led the design and implementation of ML solutions, especially those that involved data privacy and security.
✨Collaborative Spirit
This position involves working closely with leadership and mentoring others. Highlight your experience in collaborative environments and how you've successfully worked with cross-functional teams. Share stories that showcase your ability to influence technical direction without direct management.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's approach to federated data infrastructure and their strategies for data privacy. This shows your genuine interest in the role and helps you understand how you can contribute to their mission-driven goals.