Genotype-to-Phenotype ML Scientist (Computational Biology)

Colossal Biosciences; Dallas (TX); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Medical coverage

Dental coverage

Vision coverage

Paid time off

Flexible Spending Accounts (FSA)

401k retirement plan

Paid parental leave

Education reimbursement

Job summary

Colossal Biosciences seeks a talented computational biologist to tackle genotype-to-phenotype questions and integrate multi-omics datasets for de-extinction efforts. You will collaborate with scientists and engineers to design bioinformatics analyses and develop tools in R/Python.

The role emphasizes ML/AI frameworks, data integration across genomic, epigenomic, transcriptomic, and proteomic datasets, and collaboration with cross-functional teams.

Qualifications

• Two years of bioinformatics experience in Applied Statistics or ML/AI in Genomics, Comparative Genomics, Functional Genomics/Multi-Omics, or Molecular Evolution.
• Demonstrated ability building/training ML/AI models linking genotype and phenotype with libraries like PyTorch, TensorFlow, or OpenCV.
• Proficiency with scripting languages (Python, R, Perl, Ruby, Java, BASH).

Responsibilities

• Build ML/AI models using diverse integrated datasets.
• Run comparative, functional, and statistical genomics analyses.
• Develop new data analysis tools in R/Python and visualize results.
• Curate and document data and analysis workflows.
• Prepare reports to communicate findings to wet-lab biologists and leadership.

Skills

Bioinformatics

ML/AI in Genomics

Python & R scripting

HPC/Cloud computing

Data integration

Education

Master's degree + 2 years experience

PhD preferred

Tools

Git

SQL databases

MySQL

PostgreSQL

MongoDB

Job description

Colossal Biosciences seeks a talented computational biologist to tackle genotype-to-phenotype questions and integrate multi-omics datasets for de-extinction efforts. You will collaborate with scientists and engineers to design bioinformatics analyses and develop tools in R/Python.

The role emphasizes ML/AI frameworks, data integration across genomic, epigenomic, transcriptomic, and proteomic datasets, and collaboration with cross-functional teams.