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Bioptimus
15d ago

Biology Data Quality Engineer(m/w/x)

Berlin
Full-timeWith Home OfficeExperienced
AI/ML
Data Science

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.

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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

Master's degree

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

PythonmatplotlibAWS

Languages

EnglishBusiness 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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