NexaCare Systems · AI Engineer for Healthcare
Health Data Labelling Analyst
6 weeks · 10 hrs/week · Remote · Foundation
NexaCare Systems is a fictional simulated work environment created for the Jay Health Work Experience Programme. It is not a real or verified employer, and this programme is an educational work-experience placement, not employment.
Programme overview
Focus on consistent health-data labelling, using clear label definitions, uncertainty handling and agreement checks to create a traceable annotation process.
Health Data Labelling Analyst Work Lab
The Health Data Labelling Analyst Work Lab programme scope centres on consistent decision labels on routine claims. Its intended outputs include labelled case dataset specification, label agreement and discrepancy report, revised labelling guidelines.
What you will do: Health Data Labelling Analyst Work Lab
- Apply label definitions to healthcare case examples
- Compare labels and investigate disagreements
- Record uncertain examples and guideline updates
Through role-focused educational assignments at NexaCare Systems, you will work through case briefs, document the evidence behind your decisions and prepare work samples relevant to consistent decision labels on routine claims.
Work you will prepare for review
- Labelled case dataset specification
- Label agreement and discrepancy report
- Revised labelling guidelines
Methods you will practise
- Labelling rubrics
- Agreement and discrepancy worksheets
Your weekly work cycle
- Develop and revise your labelled case dataset specification
- Submit progress on your label agreement and discrepancy report with evidence and outstanding questions
- Discuss consistent decision labels on routine claims findings in a supervisor review and record agreed next steps
Feedback and improvement. Your Health Data Labelling Analyst assignments will be reviewed against the programme’s assessment criteria. You will explain your approach, discuss corrections and refine your deliverables before including permitted samples in your portfolio. The programme runs for 6 weeks, with an expected commitment of 10 hours per week; agreed assignments and review arrangements are confirmed in your enrolment offer.
Role description
Purpose of the role. The Health Data Labelling Analyst programme develops role-specific judgement and professional work samples through consistent decision labels on routine claims. The emphasis is on explaining your approach, producing clear evidence and improving work in response to supervisor feedback.
Work environment. A remote educational Health Data Labelling Analyst programme set in the fictional NexaCare Systems environment, with role-focused case briefs, structured assignments and supervisor review.
Day-to-day responsibilities
- Apply label definitions to healthcare case examples
- Compare labels and investigate disagreements
- Record uncertain examples and guideline updates
Weekly responsibilities
- Develop and revise your labelled case dataset specification
- Submit progress on your label agreement and discrepancy report with evidence and outstanding questions
- Discuss consistent decision labels on routine claims findings in a supervisor review and record agreed next steps
Expected deliverables
- Labelled case dataset specification
- Label agreement and discrepancy report
- Revised labelling guidelines
Reporting and supervision. The Health Data Labelling Analyst programme scope includes an assigned supervisor reviewing role-specific deliverables, explaining corrections and assessing work against the criteria in your enrolment offer.
Tools and methods relevant to this programme
- Labelling rubrics
- Agreement and discrepancy worksheets
Requirements
Qualifications or professional background
- Computer science, data science, engineering or health-informatics background
Required skills
- Analytical thinking
- Data literacy
- Clear technical writing
Preferred skills
- Python or SQL familiarity
- Interest in health insurance data
Previous experience. No previous experience required.
Digital literacy. Comfortable using web applications, spreadsheets and online communication tools.
Communication. Clear written English and timely responses to team messages and supervisor feedback.
Availability. 10 hours per week for 6 weeks, including scheduled reviews.
Technical requirements. A laptop or desktop computer, a modern browser and a stable internet connection.
Professional and ethical boundaries
- Educational casework only; no employment, real patient care, payer transactions or production system access is implied
- Use only fictional or authorised educational material; do not upload real patient or confidential employer information
- Specialist software, live integrations and dedicated role-specific workflows are not included in the current platform
Expected workload
Hours per week. 10 hours.
Assignment scope. Role-specific case briefs and deliverables focused on consistent decision labels on routine claims. Assignment volume and due dates are confirmed in the enrolment offer within the 10-hour weekly commitment.
Deadlines and reporting. Tasks carry individual due dates; a weekly summary is due at the end of each week.
Meetings. One weekly team stand-up and one supervisor review session.
Attendance. Check in and out of the work lab for each working session.
How performance is evaluated
- Accuracy and relevance of labelled case dataset specification
- Evidence and reasoning in label agreement and discrepancy report
- Documentation quality and professional boundaries
- Deadline adherence and response to feedback
Benefits
- Practical work-experience programme
- Access to Remote Career Launchpad
- Free mentor access according to the programme's mentoring arrangements
- Access to the Jay Health job board
- 10% discount on eligible Jay Health courses
- Supervisor feedback
- Opportunity to build a portfolio of permitted work samples
- Performance-based reward eligibility, where applicable (conditional, not guaranteed)
Learning outcomes
By the end of the programme you should be able to:
- Explain the key decisions and standards involved in consistent decision labels on routine claims
- Prepare professional labelled case dataset specification
- Prepare professional label agreement and discrepancy report
- Prepare professional revised labelling guidelines
- Use supervisor feedback to improve the clarity, accuracy and completeness of your work
Selection process
- Application review
- Interview
- Role-relevant assessment
- Acceptance decision
- Enrolment and payment of the ₦20,000 enrolment fee (only after acceptance)
- Onboarding