MediOrbit Systems · AI Engineer for Healthcare
Healthcare AI Data Associate
3 months · 15 hrs/week · Remote · Intermediate
MediOrbit 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
Develop healthcare AI data preparation skills by examining claim information, defining annotation guidelines and documenting data quality issues that affect downstream analysis.
Healthcare AI Data Associate Work Lab
The Healthcare AI Data Associate Work Lab programme scope centres on labelling claims decisions with rationale. Its intended outputs include annotated healthcare example set, data quality issue register, annotation guidance and provenance notes.
What you will do: Healthcare AI Data Associate Work Lab
- Review healthcare data fields for completeness
- Annotate decision evidence using a defined guide
- Document quality issues and data provenance
Through role-focused educational assignments at MediOrbit Systems, you will work through case briefs, document the evidence behind your decisions and prepare work samples relevant to labelling claims decisions with rationale.
Work you will prepare for review
- Annotated healthcare example set
- Data quality issue register
- Annotation guidance and provenance notes
Methods you will practise
- Data dictionaries
- Annotation and data quality worksheets
Your weekly work cycle
- Develop and revise your annotated healthcare example set
- Submit progress on your data quality issue register with evidence and outstanding questions
- Discuss labelling claims decisions with rationale findings in a supervisor review and record agreed next steps
Feedback and improvement. Your Healthcare AI Data Associate 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 3 months, with an expected commitment of 15 hours per week; agreed assignments and review arrangements are confirmed in your enrolment offer.
Role description
Purpose of the role. The Healthcare AI Data Associate programme develops role-specific judgement and professional work samples through labelling claims decisions with rationale. The emphasis is on explaining your approach, producing clear evidence and improving work in response to supervisor feedback.
Work environment. A remote educational Healthcare AI Data Associate programme set in the fictional MediOrbit Systems environment, with role-focused case briefs, structured assignments and supervisor review.
Day-to-day responsibilities
- Review healthcare data fields for completeness
- Annotate decision evidence using a defined guide
- Document quality issues and data provenance
Weekly responsibilities
- Develop and revise your annotated healthcare example set
- Submit progress on your data quality issue register with evidence and outstanding questions
- Discuss labelling claims decisions with rationale findings in a supervisor review and record agreed next steps
Expected deliverables
- Annotated healthcare example set
- Data quality issue register
- Annotation guidance and provenance notes
Reporting and supervision. The Healthcare AI Data Associate 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
- Data dictionaries
- Annotation and data quality 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. Prior exposure is helpful but not essential.
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. 15 hours per week for 3 months, 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. 15 hours.
Assignment scope. Role-specific case briefs and deliverables focused on labelling claims decisions with rationale. Assignment volume and due dates are confirmed in the enrolment offer within the 15-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 annotated healthcare example set
- Evidence and reasoning in data quality issue register
- 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 labelling claims decisions with rationale
- Prepare professional annotated healthcare example set
- Prepare professional data quality issue register
- Prepare professional annotation guidance and provenance notes
- 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