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QED.ai

Data systems and AI for health.

Data Scientist

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeMalawiNo salary listedIn-person

About the Job

Requirements

  • At least 3 years of experience in machine learning, preferably in computer vision.
  • A track record in competitive data modeling and/or notable research achievements.
  • Knowledge of current trends, high-performing tools, and the machine learning research community.
  • Fluency in Python and the main artificial intelligence-related tools.
  • Strong logical reasoning and critical-thinking skills, confidence expressing ideas, and willingness to receive constructive feedback through daily code review.
  • A substantive understanding of artificial intelligence beyond using black-box tools.
  • Fluency in spoken and written English, with typing speed above 35 words per minute.
  • Critical-thinking skills, expressiveness, and a team-oriented attitude.

Desired Qualifications

  • Working knowledge of statistics and basic probability.
  • Experience with enterprise data warehouse software and/or data processing pipelines.
  • Experience working with datasets too large to fit on a desktop, and systems-engineering techniques for overcoming these constraints.
  • Leadership and team-building skills.

About the Company

QED.ai

Data systems and AI for health.

QED.ai builds data systems and AI solutions for health and agriculture to support humanitarian projects aligned with SDGs 2, 3, 6, and 15. It designs and deploys data platforms that help partner organizations collect, store, and analyze data, then uses AI-driven insights to inform policy decisions. Unlike generic tech vendors, it tailors its tools to local conditions and works closely with international projects to ensure real-world applicability. Its goal is to empower development actors with practical data-enabled tools that improve health, agriculture, and environmental outcomes while strengthening policy impact in low-resource settings.

Company size
11-50
Company stage
Not listed
Total funding
Not listed
Headquarters
Berkeley, CA
Founded
2008

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