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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 opportunities for significant technical influence.
- 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 in Slough 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 Slough
✨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 us see your hands-on experience with ML solutions. This can really set you apart from the crowd.
✨Tip Number 3
Prepare for those interviews! Research common questions for Principal Machine Learning Scientist roles and practice your responses. We recommend focusing on your technical expertise and how you’ve tackled challenges in previous projects. Confidence is key!
✨Tip Number 4
Apply through our website! We make it super easy for you to submit your application directly. Plus, it shows your enthusiasm for the role and the company. Don’t miss out on the chance to join a mission-driven team in drug discovery!
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 Slough
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 research papers, open-source contributions, or personal projects, we want to see how you’ve applied your skills in real-world scenarios, especially in ML and data security.
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 gives you a chance to explore more about our company culture and values!
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 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
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.
✨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.