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- Tasks: Lead groundbreaking analyses in genetics and collaborate with diverse teams to drive drug discovery.
- Company: Cutting-edge techbio on a mission to revolutionise health resilience and therapies.
- Benefits: Competitive salary, innovative projects, and opportunities for professional growth.
- Other info: Dynamic environment with a focus on collaboration and cutting-edge technology.
- Why this job: Join a pioneering 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 Brighton 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 Brighton
✨Tip Number 1
Get your networking game on! Connect with professionals in the field of statistical genetics and AI. Attend relevant conferences or webinars, and don’t be shy to reach out on LinkedIn. We all know that sometimes it’s not just what you know, but who you know!
✨Tip Number 2
Prepare for those interviews like a pro! Research the company and their projects, especially around health resilience and drug development. Be ready to discuss how your skills in Python, R, and ML can contribute to their mission. We want you to shine!
✨Tip Number 3
Showcase your work! If you’ve got any projects or papers, make sure to have them handy. Discussing your hands-on experience with large-scale genetic datasets and GWAS methodologies will definitely impress. We love seeing real examples of your expertise!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we’re always looking for passionate individuals who are eager to drive innovation in the techbio space. Let’s get you that job!
We think you need these skills to ace Statistical Geneticist - ML in Brighton
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 specific projects where you've led analyses or built pipelines for population-scale datasets. Highlight any first authorships on published papers, as this will demonstrate your expertise and commitment to the field.
✨Collaborative Spirit
Since the role involves working cross-functionally with ML, biology, and engineering teams, be ready to share examples of how you've successfully collaborated with others. This could include discussing how you integrated multi-omics QTL data or worked on drug discovery decisions.
✨Technical Proficiency
Familiarise yourself with the tools and languages mentioned, like Python, PyTorch, R, and Unix/Linux. Be prepared to discuss your experience with HPC or cloud computing, and if possible, bring examples of how you've used these technologies in your work.