Scientist II, Bioinformatics, Machine Learning

Key Skills

AWS, Framework, Git, Linux Operating system, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

Tome Biosciences is the programmable gene insertion company. Using CRISPR, our technologies allow us to insert any genetic sequence of any size at any location into any genome. We are writing the final chapter in genomic medicines, delivering cures to patients through cell and genome engineering.

Tome is seeking a talented Scientist II, Bioinformatics, Machine Learning candidate to join our team and play a crucial role in the expansion of our platform. In this role, you will be responsible for developing innovative pipelines, analyzing omics data, and using data science tools to optimize and scale our operations. We value creativity, problem-solving ability, and adaptability, and are looking for someone who can thrive in this dynamic and constantly evolving field. As a Bioinformatics Machine Learning Scientist at Tome, you will have the opportunity to contribute to the design and evolution of our core libraries and platform.

Responsibilities:

• Manage and automate storing and integrating data into databases hosted in the Cloud.
• Interpret omics data and create compelling graphical visualizations for presentations.
• Collaborate with the team to contribute to the further development of computational infrastructure.
• Help with the development of machine learning predictive models that inform future expansion of the guide RNA designs to optimize gene insertion efficacies.
• Partner with the Genomics teams to develop experimental hypotheses and help design computational pipelines to analyze the outcomes.

Required qualifications:

• Sc. or Ph.D. in STEM disciplines. A strong analytical background is highly desired.
• Basic knowledge in one or more programming languages is required. Our primary codebase is in Python.
• Motivated and team oriented, with the ability to thrive in a multidisciplinary environment.
• Excellent communication and presentation skills. Must be able to speak “biology” and “programming.”
• Must be able to think independently and work collaboratively.

Preferred qualifications:

• Experience with metagenomics data and handling and analyzing large multiomics datasets is a big plus.
• Familiarity with development of machine learning models is a big plus (Tensorflow or Pytorch frameworks).
• Experience working within a cloud computing environment (AWS).
• Knowledge of SQL and data architecture engineering is a big plus.
• Experience with very basic software development best practices (e.g., git).
• Familiarity with Linux shell scripts and other coding languages such as R, JavaScript or Scala is a plus.