Biomedical Omics Data Scientist II

Biomedical Omics Data Scientist II

Full-Time 60750 - 74250 Β£ / year (est.) No working from home possible
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At a Glance

  • Tasks: Design and build scalable pipelines for genomic and clinical data to advance cancer research.
  • Company: Join Axle Energy, a leader in biomedical innovation and research.
  • Benefits: Enjoy competitive pay, health benefits, and opportunities for professional growth.
  • Other info: Be part of a dynamic team dedicated to impactful scientific advancements.
  • Why this job: Make a real difference in cancer research while working with cutting-edge technology.
  • Qualifications: Experience in data science, statistical methods, and collaboration with diverse teams.

The predicted salary is between 60750 - 74250 Β£ per year.

Axle Energy seeks a Data Scientist II to advance cancer research at NIH/NCI CBII/T through building scalable computational infrastructure. You will handle full omics data lifecycles, integrate modalities, and collaborate with scientists, data engineers, and government stakeholders to ensure reproducible, containerized, and well-documented workflows.

Responsibilities include:

  • Designing pipelines across genomic, transcriptomic, and clinical datasets
  • Applying statistical and ML methods

Biomedical Omics Data Scientist II employer: Axle Energy

Axle Energy is an exceptional employer, offering a dynamic work environment that fosters collaboration and innovation in the energy sector. With a hybrid office model and a dog-friendly culture, employees enjoy a supportive atmosphere while having access to equity opportunities and professional growth. Join us in making a meaningful impact on the transition to a low-carbon world, where your contributions directly influence our success.

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Contact Details:

Axle Energy Recruitment Team

We think you need these skills to ace Biomedical Omics Data Scientist II

Data Science
Omics Data Management
Computational Infrastructure Development
Pipeline Design
Genomic Data Analysis
Transcriptomic Data Analysis
Clinical Data Integration