At a Glance
- Tasks: Lead groundbreaking analyses in genetics and collaborate with diverse teams to drive drug discovery.
- Company: Join a cutting-edge techbio on a mission to revolutionise health resilience.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on collaboration and innovation.
- Why this job: Make a real impact in healthcare by unlocking novel therapies through innovative research.
- 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
- Working with and 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 Stoke-on-Trent employer: Searches @ Wenham Carter
Wenham Carter offers an exceptional work environment for a Statistical Geneticist, where innovation meets collaboration in the vibrant techbio sector. Employees benefit from a culture that prioritises professional growth, with opportunities to engage in groundbreaking research and development alongside leading pharma partners. Located in a dynamic area, the company fosters a supportive atmosphere that encourages creativity and the pursuit of novel therapies, making it an ideal place for those seeking meaningful and impactful careers.
StudySmarter Expert Advice🤫
We think this is how you could land Statistical Geneticist - ML in Stoke-on-Trent
✨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 message can go a long way in getting your foot in the door.
✨Tip Number 2
Prepare for those interviews! Brush up on your knowledge of GWAS, PheWAS, and multi-omics data integration. Be ready to discuss how your experience with Python, R, and machine learning can contribute to their mission of driving drug discovery.
✨Tip Number 3
Showcase your projects! If you've worked on relevant research or have hands-on experience with large-scale genetic datasets, make sure to highlight these during your conversations. Bring along examples that demonstrate your analytical skills and problem-solving abilities.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in the role and the company. Keep your CV updated and tailored to reflect the skills they’re looking for.
We think you need these skills to ace Statistical Geneticist - ML in Stoke-on-Trent
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 candidates who can hit the ground running, so let us know what tools you’re comfortable with!
Be Clear and Concise:When writing your application, keep it straightforward. We appreciate clarity, so avoid jargon unless it’s necessary. Make it easy for us to see how your background fits the role without wading through unnecessary fluff.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the position. Plus, it gives you a chance to check out 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 methodologies 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 experience and analytical skills.
✨Collaborative Spirit
Since the role involves working cross-functionally, think of examples where you've successfully collaborated with teams from different backgrounds, like biology or engineering. Share how you communicated complex ideas effectively and contributed to joint goals.
✨Technical Proficiency
Be ready to discuss your proficiency in Python, R, and any experience with cloud computing or HPC. You might be asked to solve a technical problem on the spot, so practice coding challenges related to statistical genetics and machine learning to boost your confidence.
