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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 work environment, and opportunities for professional growth.
- Other info: Dynamic role with excellent career advancement opportunities in a collaborative setting.
- Why this job: Join a pioneering team using AI and genetics to make a real impact in healthcare.
- Qualifications: MSc or PhD in relevant fields and 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
- 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 Chesterfield 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 Chesterfield
✨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 the interview by brushing up on your technical skills. Make sure you can confidently discuss your experience with GWAS, multi-omics data, and machine learning. We want to see you shine when it comes to explaining how you've applied these in real-world scenarios.
✨Tip Number 3
Showcase your projects! If you’ve worked on any relevant research or personal projects, be ready to share them. Whether it’s a GitHub repo or a published paper, having tangible examples of your work can really set you apart from other candidates.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to engage directly with us. Let’s get you one step closer to that Statistical Geneticist role!
We think you need these skills to ace Statistical Geneticist - ML in Chesterfield
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 looking for someone who can hit the ground running, so let us know how you’ve used these tools in your past work.
Highlight Collaborative Experience: This position requires working cross-functionally with various teams. Share examples of how you've collaborated with others, especially in ML or biology contexts. We love seeing candidates who can communicate effectively across disciplines!
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 the role. Plus, it gives you a chance to explore more about our mission and values!
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. This will demonstrate your practical skills and analytical background.
✨Familiarise with the Tech Stack
Since the role requires proficiency in Python/PyTorch, R, and Unix/Linux, make sure you're comfortable discussing these tools. You might even want to prepare a few examples of how you've used them in past projects, particularly in relation to deep learning or cloud computing.
✨Collaborative Mindset
This position involves working cross-functionally with various teams. Be prepared to share examples of how you've successfully collaborated with others, whether in ML, biology, or engineering. Emphasising your teamwork skills will show that you're a great fit for their culture.