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- Tasks: Lead groundbreaking analyses in genetics and collaborate with diverse teams to drive drug discovery.
- Company: Cutting-edge techbio focused on health resilience and innovative therapies.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic environment with a focus on collaboration and innovation.
- Why this job: Join a mission-driven team using AI to unlock novel therapies and make a real impact.
- Qualifications: MSc or PhD in relevant fields and hands-on experience with large-scale genetic datasets.
The predicted salary is between 60000 - 80000 £ per year.
Wenham Carter are partnered with a cutting-edge techbio, on a mission to model health resilience, unlocking novel therapies. Combining large-scale human genetics, multi‑omics, and AI, they are driving target discovery and accelerating drug development in partnership with leading pharma companies. They are hiring a Statistical Geneticist with a strong analytical background to a key role in their genetics‑driven causal AI platform:
- Responsibilities
- Lead GWAS, PheWAS, PRS, rare variant and post‑GWAS analyses
- Integrate multi‑omics QTL data (eQTL, pQTL, mQTL) for gene prioritisation and causal inference
- Apply ML‑derived and continuous phenotypes to enhance genetic discovery
- Build scalable, reproducible pipelines for population-scale datasets
- Work cross‑functionally with ML, biology, and engineering teams to drive drug discovery decisions
- What we’re looking for
- MSc or PhD in Statistical Genetics, Bioinformatics, Biostatistics, or similar. First authorships on published papers particularly interesting
- Hands-on experience with large-scale genetic datasets
- Demonstrated skills & experience in Python/PyTorch, R, Unix/Linux, and GWAS methodologies
- Experience with HPC or cloud computing
- Experience in deep learning or ML.
Please apply with an updated CV to be considered for the position. Be prepared to talk through your skills and experience which aligns with the position.
Statistical Geneticist - ML in Edinburgh employer: Searches @ Wenham Carter
Contact Detail:
Searches @ Wenham Carter Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Statistical Geneticist - ML in Edinburgh
✨Tip Number 1
Get your networking game on! Reach out to professionals in the field of statistical genetics and AI. Use platforms like LinkedIn to connect with people at Wenham Carter or similar companies. A friendly chat can sometimes lead to opportunities that aren’t even advertised!
✨Tip Number 2
Prepare for those interviews! Brush up on your knowledge of GWAS, PheWAS, and multi-omics data integration. Be ready to discuss your hands-on experience with Python and R, and how you’ve applied ML techniques in your previous work. Confidence is key!
✨Tip Number 3
Showcase your projects! If you’ve worked on any relevant research or personal projects, make sure to highlight them. Create a portfolio or GitHub repository to demonstrate your skills in building scalable pipelines and using deep learning in genetic analysis.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Tailor your CV to highlight your experience with large-scale datasets and cloud computing, and make sure it aligns with what they’re looking for in a Statistical Geneticist.
We think you need these skills to ace Statistical Geneticist - ML in Edinburgh
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in statistical genetics and any relevant projects you've worked on. We want to see how your skills align with the role, so don’t be shy about showcasing your first authorships and hands-on experience with large-scale datasets!
Showcase Your Technical Skills: Since this role involves a lot of technical work, be sure to mention your proficiency in Python, R, and any experience with GWAS methodologies. We’re keen to see how you’ve applied these skills in real-world scenarios, so include specific examples where possible.
Highlight Collaborative Experience: This position requires working cross-functionally, so if you’ve collaborated with ML, biology, or engineering teams before, let us know! Share examples of how you’ve contributed to team projects and driven decisions in drug discovery.
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. Don’t forget to prepare for a chat about your skills and experiences that match the role!
How to prepare for a job interview at Searches @ Wenham Carter
✨Know Your Stuff
Make sure you brush up on your knowledge of GWAS, PheWAS, and the various analyses mentioned in the job description. Be ready to discuss your hands-on experience with large-scale genetic datasets and how you've applied ML techniques in your previous work.
✨Showcase Your Projects
Prepare to talk about your first authorships and any relevant projects you've worked on. Highlight how your contributions have driven results, especially in integrating multi-omics data or building scalable pipelines. Real examples will make you stand out!
✨Get Technical
Since the role requires skills in Python/PyTorch, R, and Unix/Linux, be prepared for technical questions or even a coding challenge. Brush up on your coding skills and think of ways to demonstrate your proficiency during the interview.
✨Collaborative Mindset
This position involves working cross-functionally with various teams. Be ready to discuss how you've successfully collaborated with others in the past, particularly in a research or tech environment. Show that you're a team player who can drive drug discovery decisions together with others.