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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 innovation.
- Benefits: Competitive salary up to £160,000, early equity, and comprehensive benefits.
- Other info: Collaborative environment with significant influence over technical direction.
- Why this job: Make a real impact in drug discovery while working remotely across Europe.
- Qualifications: Experience in ADMET or Structural Biology modelling and machine learning.
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 Nottingham 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 Nottingham
✨Tip Number 1
Network like a pro! Reach out to your connections in the life sciences and machine learning fields. Attend relevant webinars or meetups, and don’t be shy about asking for introductions to people at companies you're interested in.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to ADMET or Structural Biology. Use platforms like GitHub to share your code and models, and make sure to highlight any innovative solutions you've developed.
✨Tip Number 3
Prepare for those interviews! Research common questions for Principal Machine Learning Scientist roles and practice your responses. Be ready to discuss your experience with ML architecture and data privacy, as these are key areas for this position.
✨Tip Number 4
Apply through our website! We’ve got a streamlined application process that makes it easy for you to showcase your talents. Plus, we love seeing candidates who take the initiative to apply directly!
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 Nottingham
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 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 the design and implementation of ML solutions, and highlight any challenges you overcame.
✨Emphasise Data Privacy Knowledge
Given the focus on data security and privacy, make sure to discuss your understanding of privacy attack-surface assessment and how it relates to model development. This will show that you’re not just technically savvy but also aware of the ethical implications of your work.
✨Be a Team Player
Even though this isn't a people management position, collaboration is key. Share your experiences mentoring others or working closely with cross-functional teams. Highlight how you can influence technical direction while fostering a collaborative environment.