# Senior Product Manager, Data at Certifyos > Remote · India · Senior · Product · open. Source: https://alderhq.app/jobs/ashby%3Acertifyos%3A5dd98d4a-8885-4ff4-869a-fb16cd91a01c (read 2026-11-06). ## Facts (observed) - Company: [Certifyos](https://alderhq.app/companies/certifyos) - Location text: Bengaluru, India - Where it can be done from (inferred): Remote · India (evidence: "Location lists "bengaluru" and remote") - Posted: 2026-11-06 (0 days ago) - First seen by alder: 2026-11-06 · last verified: 2026-11-06 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/certifyos/5dd98d4a-8885-4ff4-869a-fb16cd91a01c ## Pay and sponsorship - Posted pay: not posted ## Description (the employer's text) ### About CertifyOS CertifyOS is building the data infrastructure that powers modern healthcare. Today, healthcare organizations rely on fragmented and outdated provider data. This creates unnecessary administrative work, regulatory risk, and higher costs across the system. We’re solving that problem. Our API-first platform automates provider licensing, enrollment, credentialing, and network monitoring by connecting directly to hundreds of primary data sources. We help healthcare organizations maintain accurate, compliant, and reliable provider networks at scale. Our vision is simple: One API. One provider ID. Frictionless provider data. We’re backed by leading investors and built by a team with deep experience in provider data systems. At CertifyOS, we value authenticity, accountability, collaboration, results, and openness to feedback. We’re building a high-ownership team focused on solving real infrastructure problems that impact millions of patients. ### About the Role CertifyOS is building the data infrastructure for the next generation of provider technology. As we scale across Credentialing, Monitoring, Rosters, Provider Network Operations, the biggest bottleneck is no longer just building features - it is making the platform easier for internal teams and customers to configure, operate, and extend safely. As the Senior Technical Product Manager, Data, you will own the strategy and execution for the system that keeps credentialed practitioners continuously compliant with NCQA standards. You'll be accountable for the end-to-end data provisioning experience, the data elements, the quality of that data, and the source veracity that proves historical compliance during audits. The ideal candidate would be comfortable making all provider data pipelines trustworthy, fast, configurable, and cheap to run at scale, while moving the team from a service-driven, manual-review posture toward a productized, self-serve, AI-native one. This role sits at the intersection of platform thinking and technical product expertise. You'll partner closely with the Application Product team, Operations, Implementation and Client Success teams. ### What You Will Do Sourcing: getting the data Own "detect need" and source routing. Define what triggers a data request (a new file, a monthly monitoring cycle, an expiring element) and the rules that route it to the best source: bulk file, API, scraper, paid vendor, outreach or a human analyst. Every attempt and its raw response is recorded. Own source economics. Know the cost, coverage and latency of every source. Decide when to build a scraper, buy an API or pay for a bulk feed. Association: getting it on the right practitioner Own attribution and matching. Define how acquired records are matched to practitioners across identifiers that are often missing or conflicting, including probabilistic matching models. Set the quality bar for false and missed matches, and own the review path for low-confidence results. Own survivorship and Master Data Management governance. Define which source wins for each attribute, and make that configurable. Golden record rules, merge and unmerge, audit trail, and a monitoring and observability loops so matching changes never ship blind. Across the chain Build explainability. Anyone in the company should be able to ask why a license is missing, stale or attached to the wrong practitioner, and get a traced answer from source to screen. Partner with engineering on the observability that makes the answer trustworthy. Measure data loss end to end. Build the attribute-level funnel that shows where data drops out between acquisition and the application, and give each application pod a data health metric tied to its own north star. Keep data pipelines stable. Triage pipeline and data escalations, separate one-off fixes from product gaps, and cut the volume of manual re-scrapes and stale-data tickets. Define metrics for data platform effectiveness: Metrics by application and client, source success rate and freshness, % of requests resolved without a human, match precision and recall, % of data errors caught before a client reports them, time to explain a data issue, and external data cost per practitioner. --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Acertifyos%3A5dd98d4a-8885-4ff4-869a-fb16cd91a01c and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.