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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: 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 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
✨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. 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 projects related to ADMET or Structural Biology. Whether it's a GitHub repo or a personal website, let your work speak for itself. We want to see your hands-on experience and innovative solutions!
✨Tip Number 3
Prepare for those interviews! Research the company and its mission in drug discovery. Be ready to discuss how your expertise in ML architecture and data privacy can contribute to their goals. We’re all about making a strong impression, so practice your pitch!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who take the initiative. So, don’t wait around – get your application in and let’s make some waves in the industry together!
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
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 specific about your technical expertise and how you can contribute to our collaborative model development.
Showcase Your Projects: If you've worked on relevant projects, make sure to include them in your application. Whether it's a paper you've published or a project where you implemented cutting-edge ML techniques, we want to see what you've done and how it relates to the role.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you get all the updates directly from us. Plus, it shows you're keen on joining our team!
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.