Dyno Therapeutics

Computational Biologist II, Capsid Data Science

Sep 12, 2023

Watertown, MA

The Company

Dyno Therapeutics is reshaping the gene therapy landscape through AI-powered vectors. Through the application of our transformative technologies and strategic partnerships with leaders in gene therapy, we believe a future with life changing gene therapies for millions of people is within reach. 

Our team includes world-class molecular and synthetic biologists, protein engineers and gene therapy scientists working alongside software engineers, data scientists, and machine learning experts to transform the landscape of available gene therapy capsids. Dyno was named one of America’s Best Startups in 2022 and 2023 by Forbes!

The Role

Computational Biologist II - Capsid Data Science. Computational Biology is at the heart of Dyno’s platform, and your work as a part of the Capsid Data Science team can have a major impact on the future of gene therapy. The Capsid Data Science team focuses on translating our data into scientific insights and iterative capsid design. The team has a central role in synthesizing data from across Dyno to identify and evaluate top capsids in the context of our product goals. Computational scientists and engineers on the team work together to generate data-driven insights and outputs that drive machine learning models and strategic decision-making.

How You Will Contribute

As a Computational Biologist II on Capsid Data Science, you will lead analysis of biological data in a statistically rigorous manner and communicate findings to stakeholders across Dyno. This is a highly collaborative position working closely with other scientists and stakeholders to enable decision-making based on the data collected at Dyno. 


  • Analyze and explore large, complex datasets to evaluate capsid performance
  • Develop statistical and analysis methods for interpreting data from high-throughput capsid studies
  • Contribute to preparing reports and presentations
  • Collaborate with software engineers to streamline data processing workflows
  • Communicate technical results and methods to scientists and stakeholders from diverse teams

Who You Are 

  • Trusted partner
  • Team oriented
  • Thoughtful & detail oriented 
  • Passion for solving problems
  • Appreciation for opportunities at the intersections of data science, machine learning, and biology
  • Thrives in a fast paced working environment 
  • Curious and not afraid to ask questions

Basic Qualifications

  • Ph.D. in computational biology, statistics, physics (or related quantitative fields) or equivalent experience
  • Strong foundation in data analysis and statistical methods 
  • Experience working with large scale biological datasets
  • Experience developing code in Python for computational workflows
  • Experience with NGS data analysis
  • Experience with data visualization and communication


Preferred Qualifications

  • Internship or work experience in an industry setting
  • Publications in peer-reviewed journals or conferences
  • Familiarity with software engineering best practices
  • Familiarity with molecular biology, protein engineering, gene therapy, and/or AAV biology
  • Demonstrated independence in leading research projects or collaborations

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. 

Dyno Tx and other biotech companies have recently been made aware of a growing number of email scams targeting job candidates in our industry. Our recruiters at Dyno use @dynotx.com email addresses exclusively. We do not ask candidates to purchase anything through us or to provide sensitive identifiable information via email (outside of a resume). If a person claiming to be a Dyno Tx recruiter with an alternative domain reaches out to you regarding a job posting, please report it to our team by emailing [email protected] with details.

Job Type: Full-time

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