Contractor, Monitoring, Evaluation and Learning at Himalayan Cataract Project
Listed on 2026-01-29
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Healthcare
Data Scientist, Healthcare Administration
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Contractor, Monitoring, Evaluation and LearningReports To:
Director, Monitoring, Evaluation & Learning (MEL)
Location:
Remote - US
Duration: 84 hours within a month
Organizational ProfileFor more than 43 million people, blindness is a daily reality; another 295 million suffer from moderate to severe vision impairment. 90% of these people live in low- and middle-income countries where access to eye care is limited and the challenges for those who can’t see are severe. But there is hope: 80% of this burden is treatable or preventable. For example, 17 million blind people could be cured with a 10-minute cataract surgery.
Millions more suffer from corneal blindness, which is often preventable or treatable. At the Cure Blindness Project, we believe avoidable blindness can – and must – be overcome. For more than 30 years, we have worked alongside a capable network of collaborators to help people around the world retain and regain their sight by developing high-quality, cost-effective eye care systems in underserved areas of the world.
With the help of partners in more than 30 countries, Cure Blindness Project has provided more than 1.6 million sight-restoring surgeries and screened more than 16.5 million people to provide care and basic treatments. We’ve trained more than 20,100 eye health professionals and have established 5 eye hospitals. There is more work left to do, but we can overcome the mountain of global blindness together.
Cure Blindness Project is expanding its team to meet new challenges, scale its work, and broaden its reach. We are seeking individuals who are passionate about changing lives and want to be a part of a team that works together to reach new heights. We want leaders who can aim high, make things happen, and are invested in building a positive team dynamic to bring the work to life.
VisionA world where no one is needlessly blind.
MissionWe enable countries to cure avoidable blindness by developing high-quality, cost-effective eye care in underserved areas of the world.
Scope of WorkAs our Contractor, Monitoring, Evaluation & Learning you'll be instrumental in supporting the Monitoring, Evaluation, and Learning department through a short-term independent contractor role. This position offers a unique opportunity to work with real-world health data to strengthen eye health programs. You’ll lead efforts to review and clean historical datasets related to patient surgeries, ensuring accuracy and consistency across records. Cleaned data will be transferred into standardized Excel templates, and you’ll also organize datasets from other program areas, such as training for eye health professionals, into relevant formats.
Your work will support data quality assurance processes and contribute to uploading standardized data into organizational MEL databases.
- Review historical datasets of patient surgeries and training programs.
- Identify and correct inconsistencies, missing values, and data entry errors.
- Migrate cleaned data into the current Version 7 of the Patient Surgical Forms.
- Ensure proper structuring of training program data in appropriate templates.
- Upload cleaned and standardized data into the organization’s MEL databases.
- Ensure data integrity and accuracy during the upload process.
- Perform quality checks to confirm successful data migration.
- Document any errors or challenges encountered during data uploads and suggest improvements.
- Perform data quality checks to validate accuracy.
- Support in documenting data quality gaps in the datasets and suggest improvements for data points with significant gaps.
- Maintain organized records of cleaned and transferred datasets
- Assist in drafting data summary reports for internal review.
- Cleaned and standardized datasets of patient surgeries and training programs.
- Data successfully migrated into the latest Excel templates and uploaded into MEL databases.
- Brief reports summarizing key findings, data quality…
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