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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 Northampton 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 Northampton
✨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 graph neural networks and transformers. 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 opportunities that might be perfect for you. Plus, it’s a great way to ensure your application gets seen by the right people.
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 Northampton
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 research paper or a personal project, showing us your practical experience with ML solutions for ADMET will definitely catch our eye!
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. 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 with ML solutions in a similar context. Talk about the challenges you faced, how you overcame them, and the impact your work had on the projects. Real-world examples will make you stand out.
✨Emphasise Data Privacy Knowledge
Given the focus on data security and privacy, it’s crucial to highlight your understanding of these concepts. Discuss any experience you have with privacy attack-surface assessments or implementing data privacy measures in your models. This will show that you’re aligned with their mission.
✨Be Ready to Collaborate
This position involves working closely with leadership and mentoring others, so be prepared to discuss your collaborative experiences. Share how you've worked with teams in the past, especially in a remote setting, and how you’ve contributed to fostering a positive team environment.