Computational Biologist I – Biological Data & Screening

Cherokee Federal; Frederick (MD); On-site; USD 85,000 - 110,000; Full time

Job summary

Cherokee Nation Integrated Health (CNIH) is seeking a Computational Biologist I to provide biological data computational support for high-content imaging and high-throughput screening activities.

The role involves collecting and integrating biological, chemical, screening, and ADME data, developing a searchable database, and analyzing phenotypic screening datasets to identify promising small-molecule candidates for therapeutic development.

Qualifications

• Experience with high-content imaging, high-throughput screening, phenotypic screening, biological assays, chemical compounds, or ADME data.
• Experience collecting, organizing, integrating, migrating, and managing biological, chemical, screening, imaging, experimental, and associated metadata.
• Experience using R and relevant R packages, spreadsheets, graphics, statistical programs, and other computational methods to analyze complex biological datasets.

Responsibilities

• Collect data from high-content imaging and screening platforms and support database development and metadata capture.
• Assess workflows to identify data attributes needed for database configuration and management.
• Integrate biological, chemical, screening, and ADME data into the database and support ongoing data management.
• Analyze screening data to identify promising molecules for therapeutic development.
• Present findings and communicate with laboratory staff, vendors, and stakeholders.

Skills

R programming/Statistics

Data analysis

Scientific communication

Tools

R

Job description

Cherokee Nation Integrated Health (CNIH) is seeking a Computational Biologist I to provide biological data computational support for high-content imaging and high-throughput screening activities.

The role involves collecting and integrating biological, chemical, screening, and ADME data, developing a searchable database, and analyzing phenotypic screening datasets to identify promising small-molecule candidates for therapeutic development.