Bioinformatics Data Analyst

100% REMOTE ROLE

Pay: $44-$49/hr

REQUIRED:

• Master's degree bioinformatics with 3+ years of relevant experience.
• Proficiency in databases (SQL) and coding in R/Python
• Proven experience working with databases (PostgreSQL proficiency).
• Experience with API development
• Familiarity with AI techniques and strong curiosity for new technologies
• Experience managing and curating bioinformatics datasets (BulkRNAseq, Proteomics, scRNAseq, CRISPR)
• Code management, documentation, and version control (e.g., GitHub)

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• Advanced skills in Python and R for automation and data manipulation.
• Experience handling and curating bioinformatics datasets (BulkRNAseq, Proteomics, scRNAseq, CRISPR).
• Code management, documentation, and usage of Github.
• Curiosity and basic knowledge of AI techniques applicable to data loading and querying.
• Excellent communication skills and a collaborative mindset.
• Demonstrated experience with AWS resources.
• Experience in API development and data integration.

Job Description:

As a Bioinformatics Data Analyst, you'll drive data quality and consistency in our central hub for storing OMICS data, address impactful data loading and curation projects and help improve and automate processes using agentic AI. Working closely with researchers, you'll ensure their data needs are met and help accelerate scientific discovery.

Key Responsibilities:

• Contribute to important data loading and curation projects for the departments Omics data server
• Address data quality and consistency issues in the CRISPR database.
• Apply agentic AI approaches for data loading and querying OMICS data
• Database Interaction: Use PostgreSQL to build, manage, and query large genomic datasets.
• API Development: Design and implement APIs for improved data accessibility and integration across platforms.
• Automation: Use Python and R to automate and optimize data workflows, prioritizing data quality and integrity.
• ETL Process Management: Develop and execute ETL processes to integrate high-value datasets in line with organizational standards.
• Collaboration: Work with cross-functional teams and research scientists to gather requirements, align to common data model standards, and facilitate effective data management.
• Documentation: Maintain comprehensive documentation and version control for reproducibility and teamwork.