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- Tasks: Lead ML solutions for drug discovery, focusing on data privacy and innovative modelling techniques.
- Company: Mission-driven tech company in life sciences, fully remote opportunities across EU/UK.
- Benefits: Competitive salary up to £160,000, early equity, and comprehensive benefits.
- Other info: Collaborative environment with significant influence over technical direction and career growth.
- Why this job: Make a real impact in drug discovery while advancing your career in a cutting-edge field.
- Qualifications: Experience in machine learning, computational chemistry, and strong problem-solving 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 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.
- Author or contribute to scientific publications or open-source software where appropriate.
Principal Machine Learning Researcher (Privacy/Risk) | Series A - Drug discovery Platform | Fully Remote, EU/UK | Base Salary , plus early equity+benefits in Bolton 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 Researcher (Privacy/Risk) | Series A - Drug discovery Platform | Fully Remote, EU/UK | Base Salary , plus early equity+benefits in Bolton
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend virtual meetups, and connect with potential colleagues on LinkedIn. We all know that sometimes it’s not just what you know, but who you know that can land you that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to machine learning and data privacy. We recommend sharing your work on platforms like GitHub or even writing a blog post about your findings. It’s a great way to demonstrate your expertise!
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios specific to machine learning and drug discovery. We suggest doing mock interviews with friends or using online platforms to get comfortable discussing your experience and technical knowledge.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for you, and applying directly can sometimes give you an edge. Plus, it’s super easy to keep track of your applications this way!
We think you need these skills to ace Principal Machine Learning Researcher (Privacy/Risk) | Series A - Drug discovery Platform | Fully Remote, EU/UK | Base Salary , plus early equity+benefits in Bolton
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of Principal Machine Learning Researcher. Highlight your experience with ADMET, structural biology modelling, and any relevant machine learning techniques. We want to see how your skills align with our mission in drug discovery!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about data privacy and risk in machine learning. Share specific examples of your work that demonstrate your expertise and how you can contribute to our team at StudySmarter.
Showcase Your Projects: If you've worked on any projects related to ML architecture or data security, make sure to include them in your application. We love seeing hands-on experience, especially if it involves collaborative model development or innovative solutions in drug discovery.
Apply Through Our Website: We encourage you to apply directly through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to submit all your materials in one go. Plus, we love seeing candidates who take the initiative to connect with us directly!
How to prepare for a job interview at Owen Thomas | B Corp™
✨Know Your Stuff
Make sure you brush up on the latest techniques in machine learning, especially around ADMET and data privacy. Be ready to discuss your hands-on experience with graph neural networks and transformers, as well as any relevant projects you've worked on.
✨Showcase Your Collaboration Skills
Since this role involves working closely with leadership and mentoring others, be prepared to share examples of how you've successfully collaborated in past projects. Highlight any experiences where you’ve led technical discussions or contributed to team success.
✨Understand the Company’s Mission
Familiarise yourself with the company’s mission in the life sciences domain. Be ready to articulate how your skills and experiences align with their goals, particularly in advancing drug discovery while ensuring data privacy.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions during the interview. Prepare to explain your approach to model design, implementation, and privacy assessments. Practising coding problems or case studies related to ML architecture can also give you an edge.