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Biology Data Quality Engineer(m/w/x)
Description
You will ensure the integrity of complex biological datasets by building automated validation pipelines and standardizing data, playing a vital role in training foundation models.
Let AI find the perfect jobs for you!
Upload your CV and Nejo AI will find matching job offers for you.
Requirements
- •Team-first attitude and independence
- •Curiosity and detail-orientation
- •Ability to thrive in dynamic environments
- •Strong domain expertise in biology
- •Strong computational and hands-on skills
- •Deep understanding of transcriptomics data
- •Knowledge of genomics and proteomics data
- •Experience in data quality control
- •Familiarity with data validation tools
- •Strong analytical and problem-solving skills
- •Proficiency in Python
- •Knowledge of data visualization libraries
- •Excellent written and verbal communication
- •MSc in Biology, Computational Biology, or Bioinformatics
- •Experience in machine learning for histology
- •Experience working with AWS
- •Experience with data annotation guidelines
- •Experience with data ontologies
- •Experience building large-scale data collections
- •Experience in spatial alignment of datasets
Education
Work Experience
approx. 1 - 4 years
Tasks
- •Develop comprehensive data validation protocols for histology, omics, and clinical datasets
- •Design automated pipelines to streamline data validation and identify issues early
- •Establish data standardization practices for seamless integration across different data types
- •Curate biological datasets to enhance their usability for machine learning
- •Collaborate with the R&D team to address specific data quality requirements
- •Communicate findings and recommendations to technical and non-technical stakeholders
- •Coordinate and synchronize data standards with external providers
- •Document quality assessment procedures, validation results, and data specifications
- •Generate regular reports on data quality metrics and trends
- •Evaluate external public data sources for foundation model training suitability
- •Implement improvements to data quality processes based on industry best practices
Tools & Technologies
Languages
English – Business Fluent
Benefits
Flexible Working
- •Flexible work arrangements
- •Remote work options
Competitive Pay
- •Competitive salary
- •Equity package
Career Advancement
- •Professional growth opportunities
- •Leadership development opportunities
Purpose-Driven Work
- •Collaborative and mission-driven environment
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Biology Data Quality Engineer(m/w/x)
The AI Job Search Engine
Description
You will ensure the integrity of complex biological datasets by building automated validation pipelines and standardizing data, playing a vital role in training foundation models.
Let AI find the perfect jobs for you!
Upload your CV and Nejo AI will find matching job offers for you.
Requirements
- •Team-first attitude and independence
- •Curiosity and detail-orientation
- •Ability to thrive in dynamic environments
- •Strong domain expertise in biology
- •Strong computational and hands-on skills
- •Deep understanding of transcriptomics data
- •Knowledge of genomics and proteomics data
- •Experience in data quality control
- •Familiarity with data validation tools
- •Strong analytical and problem-solving skills
- •Proficiency in Python
- •Knowledge of data visualization libraries
- •Excellent written and verbal communication
- •MSc in Biology, Computational Biology, or Bioinformatics
- •Experience in machine learning for histology
- •Experience working with AWS
- •Experience with data annotation guidelines
- •Experience with data ontologies
- •Experience building large-scale data collections
- •Experience in spatial alignment of datasets
Education
Work Experience
approx. 1 - 4 years
Tasks
- •Develop comprehensive data validation protocols for histology, omics, and clinical datasets
- •Design automated pipelines to streamline data validation and identify issues early
- •Establish data standardization practices for seamless integration across different data types
- •Curate biological datasets to enhance their usability for machine learning
- •Collaborate with the R&D team to address specific data quality requirements
- •Communicate findings and recommendations to technical and non-technical stakeholders
- •Coordinate and synchronize data standards with external providers
- •Document quality assessment procedures, validation results, and data specifications
- •Generate regular reports on data quality metrics and trends
- •Evaluate external public data sources for foundation model training suitability
- •Implement improvements to data quality processes based on industry best practices
Tools & Technologies
Languages
English – Business Fluent
Benefits
Flexible Working
- •Flexible work arrangements
- •Remote work options
Competitive Pay
- •Competitive salary
- •Equity package
Career Advancement
- •Professional growth opportunities
- •Leadership development opportunities
Purpose-Driven Work
- •Collaborative and mission-driven environment
About the Company
Bioptimus
Industry
Research
Description
The company is building the first universal AI foundation model for biology to fuel breakthrough discoveries and accelerate innovation in biomedicine.
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