At a Glance
- 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 with a collaborative culture.
- Benefits: Up to £160,000 salary, attractive stock options, flexible hours, and remote work.
- Other info: Join a dynamic team with significant influence over technical direction and excellent growth opportunities.
- Why this job: Make a real impact in drug discovery while advancing your career in a cutting-edge field.
- Qualifications: Hands-on experience in ML, deep knowledge of privacy risks, and strong publication record.
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.
- Develop a working understanding of the product, federated training setup, and key life-sciences modelling use cases.
- Reproduce and extend at least one existing modelling pipeline to establish a baseline privacy and attack-surface assessment.
- Contribute to privacy analysis for one or more active federated drug discovery programs as they transition from setup into live operation.
- Hands-on experience with co-folding and structure-based models.
- Deep knowledge of federated learning and the nuances of privacy risk in distributed environments.
- You build experiments to prove (or disprove) privacy claims using quantitative and qualitative data.
- You own the "messy" problems and can explain the why behind technical risks to non-technical leaders.
- A strong publication record in ML or Computational Biology.
- Experience working within industry consortia or complex partnerships.
- Past success influencing industry standards or regulatory privacy positions.
- Fully Remote Working Culture
- Up to £160,000 Base Salary
- Attractive Stock Options
- B2B & Full time employee options
- Flexible hours + - 3 hours of CET time zone
If you think you are a good match for the role, send us your CV and if we think you are a good match, we will give you a call!
Principal Machine Learning Researcher (Privacy/Risk) | | Series A - Drug discovery Platform | Fully Remote, EU/UK | Base Salary Up to £160,000K, plus early equity+benefits employer: Owen Thomas | B CorpTM
Join a mission-driven technology company at the forefront of drug discovery, where you'll have the opportunity to lead innovative machine learning initiatives in a fully remote environment. With a strong focus on employee growth, flexible working hours, and competitive compensation including early equity, this organisation fosters a collaborative culture that values your expertise while prioritising data privacy and security. Be part of a team that not only drives scientific advancements but also supports your professional development in a dynamic and impactful field.
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 Up to £160,000K, plus early equity+benefits
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those who work at companies you're interested in. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects and research. This is your chance to demonstrate your expertise in machine learning and data privacy.
✨Tip Number 3
Prepare for interviews by brushing up on common questions related to ML architecture and data security. We want you to feel confident discussing your experience and how it aligns with the role.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love hearing from passionate candidates who are ready to make an impact in drug discovery.
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 Up to £160,000K, plus early equity+benefits
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Principal Machine Learning Researcher role. Highlight your hands-on experience with ML solutions, especially in ADMET and data privacy, to catch our eye!
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 in machine learning can contribute to our mission. Keep it engaging and relevant!
Showcase Your Publications:If you've got a strong publication record, don’t hold back! Include links or references to your work in ML or Computational Biology. This will demonstrate your expertise and commitment to the field.
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. We can’t wait to hear from you!
How to prepare for a job interview at Owen Thomas | B CorpTM
✨Know Your Stuff
Make sure you brush up on your knowledge of machine learning, especially in the context 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.
✨Showcase Your Privacy Expertise
Since this role focuses heavily on data privacy and risk, prepare to talk about your experience with federated learning and privacy attack-surface assessments. Have examples ready that demonstrate how you've tackled privacy challenges in past projects.
✨Collaborative Spirit
This position involves working closely with leadership and mentoring others. Be prepared to discuss how you've successfully collaborated in previous roles, particularly in complex partnerships or industry consortia. Highlight any experience influencing standards or regulatory positions.
✨Bring Your Research A-Game
With a strong publication record being a key requirement, be ready to discuss your research contributions. Talk about any scientific publications or open-source software you've authored or contributed to, and how they relate to the role you're applying for.
