Clinical AI Data Specialist

About the Company

A fast-growing, venture-backed company building an agentic AI layer for oncology healthcare records. The platform replaces manual clinical data review — reading pathology reports, clinical notes, and genomic panels — with AI agents that process patient records at scale to power workflows like trial matching, registry curation, and quality reporting. Trusted by leading cancer centers globally, the company has grown 10x in the past year and is backed by top-tier venture capital.

Roles and Responsibilities

• Convert clinical/RWE project requirements into clear, structured labeling guidelines for ML and GenAI systems.
• Define annotation schemas, field definitions, allowed values, evidence-span requirements, inclusion/exclusion rules, normalization rules, and ambiguity-handling logic.
• Manage labeling projects end to end — understand ML requirements, create guidelines, train annotators/reviewers, run pilot batches, review daily output, resolve ambiguity, and deliver curated datasets.
• Daily review of clinical team work across projects: check annotation consistency, missed fields, evidence selection, edge cases, guideline adherence, and recurring disagreement patterns.
• Partner with Research Engineers to understand what can be reliably extracted from notes, pathology reports, molecular reports, imaging reports, and other clinical sources.
• Partner with ML Evaluation Engineers to design gold datasets, hidden test sets, adjudication workflows, and label quality checks.
• Use GenAI tools for pre-labeling, guideline drafting, consistency review, error clustering, and data inspection.
• Use basic Python, SQL, and Excel/Sheets to inspect datasets, labels, reviewer output, and quality trends.
• Maintain guideline versioning, change logs, examples, counterexamples, and edge-case libraries.
• Act as the day-to-day bridge between ML and the clinical data team for new client projects and production improvements.

Skills and qualifications

• Background in medicine, clinical research, life sciences, pharmacy, oncology data, clinical informatics, RWE, clinical data abstraction, or healthcare data operations.
• 3–7+ years of relevant experience in clinical data curation, RWE, clinical research, oncology abstraction, registry abstraction, trial screening, clinical data management, or healthcare data operations.
• Ability to read and interpret complex clinical documents and translate them into precise annotation rules.
• Strong written communication — able to write guidelines with examples, counterexamples, edge cases, and clear decision logic.
• Basic programming/data skills: Python basics, pandas/CSV/JSON handling, SQL querying, and Excel/Google Sheets for review, QA, and project tracking.
• GenAI literacy — ability to use LLM tools for drafting, pre-labeling, review, summarization, consistency checks, and data workflows, while understanding their limitations.
• Basic ML literacy — labels, training data, validation/test sets, precision/recall, overfitting, leakage, model evaluation, and why labels must be measurable and consistent.
• Strong operational discipline for managing annotation projects, reviewer feedback loops, and versioned guideline updates.
• Ability to work cross-functionally with clinicians, annotators, ML engineers, evaluation engineers, and project stakeholders.

What's On Offer

• Real impact at scale — the systems you build directly power AI workflows that accelerate cancer research and improve patient outcomes.
• Cutting-edge, data-intensive problems at the intersection of AI and healthcare, in a highly regulated industry where reliability is non-negotiable.
• Work alongside a world-class team across AI, engineering, and product, with best-in-industry compensation.
• Fast-paced, ownership-driven culture with company-sponsored workations.
• High-ownership, lead-track IC role — strong performers may move into workstream/functional leadership within 6–12 months.
• Comprehensive health insurance, flexible working hours, and daily meal benefits.