Solutions Lead, Healthcare
Healthcare
Job Description
Company
Overview
We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data. Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech. We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI.Role Overview
As a Solutions Lead in the Healthcare vertical, you will guide prospects and customers through the definition and delivery of healthcare datasets. Your job will be to understand what customers are building, identify the data that best fits their needs, and assemble and QA high-quality samples and final deliveries that meet their technical and conceptual specs. Along the way, you’ll ensure timelines and milestones are clearly communicated from the first stages of feasibility to the final data delivery. What You Will Own - Lead solution architecture and deal design, translating customer requirements into a structured plan and driving it forward through internal project management, quality assurance, and cross-functional execution — while maintaining a customer-facing presence to ensure alignment and adapt solutions as needs evolve - Lead end-to-end program management from data specification and preparation through QA and delivery, ensuring cross-functional coordination and on-time execution - Work with Protege data partners to source cutting edge healthcare data into the Protege ecosystem - Oversee the QA, packaging, and delivery of complex datasets (EHR, claims, radiology, pathology, unstructured text), ensuring HIPAA compliance in collaboration with privacy partners Who You Are - Proven customer-facing experience: skilled at managing expectations, leading customer conversations, and delivering technical outcomes with clarity and confidence - Bring an analyst-first mindset to challenges. You are an expert in using SQL and python to query data to construct complex patient cohorts, analyze data readiness for model training, validate clinical coverage, and support other customer-specific needs - FInd satisfaction by bringing order to multiple simultaneous projects and masterfully juggle competing (and sometimes changing) priorities - Deep expertise in various healthcare data modalities ranging from EHR, claims, radiology, pathology and unstructured text - Familiarity with privacy-preserving techniques of healthcare data - Experience in healthcare AI, ML products, or enterprise data platforms - Prior startup experience - You treat those around you with kindness Why Protege - Be the connective tissue between Protege’s platform, our data, and our customers - Build datasets that directly power the next generation of AI models - Operate at the cutting edge of multimodal data — where human judgment meets machine intelligenceHow to Stand Out
- Highlight any experience building or leading secure, HIPAA‑compliant data pipelines for AI/ML in healthcare; include concrete metrics (e.g., reduced data onboarding time by 40% or cut compliance audit findings to zero) in your résumé and be ready to walk through the architecture in a diagram during the interview.
- Demonstrate mastery of the Protege stack’s likely tech stack—AWS/GCP, Terraform/IaC, Kubernetes, and privacy‑preserving tools such as differential privacy or secure multi‑party computation—by sharing a GitHub repo or a short video demo that shows you provisioning a compliant data‑exchange environment from code to production.
- Prepare a “solution brief” case study (1‑2 pages) that mirrors Protege’s core challenge: turning a fragmented, costly healthcare data source into a reusable AI training dataset. Outline the problem, your cross‑functional remote team structure, the decision‑making framework you used, and the quantifiable outcome; bring this to the interview to illustrate your outcome‑ownership mindset.
- In the interview, expect scenario questions that test ambiguity tolerance—e.g., “How would you prioritize data‑privacy vs. time‑to‑market when a partner requests rapid access?” Practice a structured answer that references risk assessment frameworks, stakeholder communication plans, and concrete mitigation steps you’ve implemented before.
- When negotiating salary/compensation, research remote‑first benchmarks for senior solutions roles in AI/healthcare (e.g., base $170‑$190k + 15‑20% equity at similar‑stage startups). Frame your ask around the specific revenue impact you can deliver (e.g., “my prior work accelerated data onboarding by X weeks, translating to $Y saved”), and be prepared to discuss a performance‑based equity vesting schedule tied to key milestones.
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