Bioptimus logo
Bioptimus

We build foundation models that will transform biology.

Biology Data Quality Engineer

QA EngineerQA EngineerFull TimeRemoteSeniorTeam 11-50Since 2024H1B No SponsorCompany SiteLinkedIn

Location

Germany

Posted

33 days ago

Salary

0

Seniority

Senior

Postgraduate DegreeEnglishPython

Job Description

Biology Data Quality Engineer

Bioptimus

• Data Validation Pipeline Development: Develop and implement comprehensive data validation protocols for diverse biological datasets (histology, omics, clinical). Ensure data integrity, consistency, and accuracy through rigorous quality checks. Design and implement automated data quality pipelines to streamline data validation and identify potential issues early in the data processing workflow. • Data Curation & Standardization: Establish and enforce data standardization practices to facilitate seamless integration and analysis across different data types. Curate datasets to enhance their usability for machine learning. • Collaboration & Communication: Work closely with the R&D team to understand data requirements and address data quality concerns. Communicate data quality findings and recommendations effectively to technical and non-technical stakeholders. Communicate and synchronize with external data providers. • Documentation & Reporting: Maintain a detailed documentation of the data-quality assessment procedures, validation results, and data specifications. Generate regular reports on data quality metrics and trends. • Data Source Evaluation: Evaluate and validate external public data sources, ensuring they meet our quality standards and are suitable for inclusion in our foundation model training. • Continuous Improvement: Stay up-to-date with the latest data quality best practices and tools in the biological domain. Propose and implement improvements to our data- quality assessment processes and pipelines.

Job Requirements

  • Omics Data Expertise. Deep understanding of transcriptomics data types (bulk, single-cell, spatial) and their specific quality considerations. Good knowledge of genomics and proteomics data.
  • Data Quality Management: Proven experience in implementing data quality control procedures and pipelines. Familiarity with data validation tools and techniques.
  • Analytical Skills: Strong analytical and problem-solving skills to identify and resolve data quality issues.
  • Programming & Data Analysis: Proficiency in Python, good knowledge of data visualization libraries (e.g. matplotlib).
  • Communication Skills: Excellent written and verbal communication skills to effectively convey data quality findings and recommendations.
  • Educational Background: MSc in Biology, Computational Biology, 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.

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