Biology Data Quality Engineer

This is a fully remote position, open to applicants in Germany.

📋 Description

• Data Validation Pipeline Development: Create and implement thorough data validation protocols for various biological datasets (histology, omics, clinical). Ensure the integrity, consistency, and accuracy of data through diligent quality checks. Design and deploy automated data quality pipelines to enhance data validation and detect potential issues early in the data processing workflow.

• Data Curation & Standardization: Develop and uphold data standardization practices to enable smooth integration and analysis across different data types. Curate datasets to improve their applicability for machine learning.

• Collaboration & Communication: Collaborate closely with the R&D team to comprehend data requirements and tackle data quality issues. Effectively communicate data quality findings and recommendations to both technical and non-technical stakeholders, while coordinating with external data providers.

• Documentation & Reporting: Keep comprehensive documentation of data quality assessment procedures, validation results, and data specifications. Produce regular reports on data quality metrics and trends.

• Data Source Evaluation: Assess and validate external public data sources, ensuring they meet our quality standards and are appropriate for inclusion in our foundational model training.

• Continuous Improvement: Remain informed about the latest best practices and tools for data quality in the biological field. Suggest and implement enhancements to our data quality assessment processes and pipelines.

⛳️ Requirements

• Omics Data Expertise: In-depth understanding of transcriptomics data types (bulk, single-cell, spatial) and their respective quality considerations. Strong knowledge of genomics and proteomics data.

• Data Quality Management: Demonstrated experience in establishing data quality control procedures and pipelines. Familiarity with data validation tools and techniques.

• Analytical Skills: Robust analytical and problem-solving abilities to identify and address data quality issues.

• Programming & Data Analysis: Proficiency in Python, along with a solid understanding of data visualization libraries (e.g., matplotlib).

• Communication Skills: Outstanding written and verbal communication skills to effectively present data quality findings and recommendations.

• Educational Background: MSc in Biology, Computational Biology, or Bioinformatics.

🏝️ Benefits

• A collaborative and mission-driven work environment.

• Competitive salary and equity package.

• Flexible work arrangements, including remote options.

• Opportunities for professional growth and leadership development.

• Shape the future of biology and AI by contributing to groundbreaking work.