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- Tasks: Lead innovative ML solutions for drug discovery while ensuring data privacy and security.
- Company: Mission-driven tech company in life sciences, focused on collaboration and innovation.
- Benefits: Up to £160,000 salary, attractive stock options, fully remote work, and flexible hours.
- Why this job: Make a real impact in drug discovery with cutting-edge technology and research.
- Qualifications: Hands-on experience in ML, federated learning, and a strong publication record.
- Other info: Join a dynamic team with significant influence over technical direction and career growth.
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 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 will 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.
3 Month Plan:
- 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.
Experience needed:
- 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.
Remuneration:
- 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 in Manchester employer: Owen Thomas | Pending B Corp™
Contact Detail:
Owen Thomas | Pending 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 Up to £160,000K, plus early equity+benefits in Manchester
✨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 sometimes lead to job opportunities that aren't even advertised.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects and research. This is a great way to demonstrate your expertise in machine learning and privacy risk, making you stand out to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on technical questions related to ML architecture and data privacy. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with both technical and non-technical folks.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love hearing from passionate candidates who are eager 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 in Manchester
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of Principal Machine Learning Researcher. Highlight your hands-on experience with ADMET or Structural Biology modelling, and don’t forget to showcase your knowledge in federated learning and privacy risk!
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 skills align with our mission. Be sure to mention any relevant publications or projects that demonstrate your expertise.
Showcase Your Problem-Solving Skills: In your application, give examples of how you've tackled complex problems in the past. We love candidates who can own 'messy' challenges and explain technical risks clearly to non-technical folks. This will set you apart!
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. Don’t miss out on the chance to join our mission-driven team!
How to prepare for a job interview at Owen Thomas | Pending B Corp™
✨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 examples of how you've tackled privacy challenges in previous projects. Be ready to explain your approach to privacy attack-surface assessment and how it relates to federated learning.
✨Demonstrate Collaboration Skills
This position involves working closely with leadership and mentoring others. Think of instances where you've successfully collaborated with teams or influenced technical direction. Highlight your ability to communicate complex ideas to non-technical stakeholders.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions during the interview. Prepare to explain your past experiments, the outcomes, and how they relate to privacy claims. Being able to articulate the 'why' behind your decisions will impress the interviewers.