Clinical Eligibility Reviewer

About RISA
RISA helps institutions succeed, beginning with leaders in oncology care. We are building an AI Operating System aligned with real-world clinical and operational workflows. Through our BOSS Console, we support oncology institutions in making more informed, efficient decisions. Our teams work at the intersection of medicine, technology and operations.
Role Summary
The Clinical Eligibility Reviewer assesses oncology patient records against clinical trial inclusion and exclusion criteria, and reviews AI-generated eligibility decisions for accuracy. This is a data-review role with no patient contact, site visits, regulatory submissions or study start-up work.
Patient Screening and Trial Eligibility Evaluation
Patient screening is the process of checking whether a patient meets every inclusion and exclusion criterion of a clinical trial protocol. A patient is eligible only if all criteria are met, so one missed detail can change the outcome. This is the core of the role, and it works in four steps:
1. Review patient data against the trial. Read the patient's records directly (pathology and imaging reports, physician notes, lab panels, medication and treatment histories) and compare them with each protocol criterion. This includes checking stage, biomarker status, prior lines of therapy, organ function, comorbidities, ECOG status and washout periods, and building the treatment timeline from documents spread across the chart.
2. Review the AI verdict. RISA's AI proposes an eligibility decision for each patient and trial. You check that decision against the source records, confirm what is correct, and identify what is wrong (for example, a misread negation, a wrong date, an outdated document or a miscounted line of therapy).
3. Record your reasoning. Give your own verdict (eligible / not eligible / insufficient data) with criterion-by-criterion rationale, citing the exact evidence in the record. Documentation must be clear enough to support audit and review by others.
4. Give structured feedback for AI learning. For every AI error, log what went wrong, where in the record the correct information is, and which criterion was affected. This feedback is used to train and improve the AI, so its quality directly shapes the system.
Key Responsibilities
● Review oncology patient records and assess eligibility against protocol criteria
● Build patient treatment timelines and resolve conflicting information across documents
● Issue an eligibility verdict with criterion-level rationale
● Review AI-generated matches and identify errors
● Submit structured feedback for model improvement
● Escalate ambiguous or borderline cases
● Maintain auditable documentation of reasoning
Required Qualifications
● Hands-on experience screening oncology patients against trial eligibility criteria (pre-screening, patient identification or eligibility review, including in a support role)
● Minimum 2 years of experience in clinical research, oncology care or a related clinical role
● Fluency in oncology terminology: TNM staging, treatment lines, biomarkers, lab thresholds, drug classes, response criteria
● Ability to interpret dense protocol criteria accurately
● Strong English reading and writing skills
● Experience working with EMR/EHR records or similar clinical data
● ICH-GCP training
Education
● MBBS or equivalent (preferred)
● BSc Nursing or BPharm with relevant experience
● MSc in Clinical Research, Life Sciences or Pharmacology
● MPH with a clinical trials focus
Preferred Qualifications
● Experience in solid tumors or hematologic malignancies (lung, breast, GI)
● Background as an oncology nurse, research nurse, clinical pharmacist, tumor registrar or coordinator at a cancer center
● Exposure to tumor boards or trial feasibility
● Experience validating output from software or AI tools
● CCRP, CCRA or equivalent certification
● CRO experience (CRA, data review) that included eligibility assessment

Work arrangement
No