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Job Overview
Location
Remote
Job Type
Full-time
Category
Product Manager
Date Posted
January 4, 2026
Full Job Description
đź“‹ Description
- • Own the end-to-end product lifecycle for SmarterDx’s AI/ML-powered clinical decision-support platform, from discovery through launch, adoption, and continuous optimization. You will translate complex clinical workflows into clear product requirements that drive measurable improvements in revenue integrity and patient outcomes.
- • Deeply immerse yourself in the day-to-day realities of hospital revenue-cycle teams, CDI nurses, and physician advisors. Conduct contextual interviews, ride-along shadowing sessions, and data-driven user research to uncover pain points that our ML models can solve—such as missed diagnoses, DRG downgrades, or documentation gaps—then prioritize the highest-impact opportunities on a quarterly OKR roadmap.
- • Partner with engineering, data engineering, and data science leaders to design experiments that test new model architectures, feature engineering techniques, and feedback loops. You will write crisp experiment charters, define success metrics (e.g., AUROC uplift, query acceptance rate, ROI per chart reviewed), and shepherd A/B tests from hypothesis to statistical significance.
- • Translate model performance dashboards into actionable narratives for non-technical stakeholders. Craft concise product briefs that explain why a 2 % increase in sensitivity at 95 % specificity translates into $1.3 M additional revenue for a 500-bed health system, ensuring buy-in from CFOs, CMOs, and frontline clinicians alike.
- • Build and maintain a living 12-18-month product roadmap that balances quick-win enhancements (e.g., faster NLP inference, UI shortcuts for power users) with strategic bets (e.g., multi-modal models that ingest imaging and genomics data). Present roadmap updates monthly to the executive team, incorporating feedback from customer advisory boards and market shifts.
- • Champion rigorous data governance and ethical AI practices. Collaborate with compliance, security, and clinical SMEs to ensure every new model meets HIPAA, SOC 2, and emerging FDA SaMD guidelines. You will draft model cards and transparency documentation that allow clients to audit algorithmic recommendations.
- • Drive cross-functional sprint rituals—stand-ups, backlog grooming, sprint planning, and retrospectives—using Jira, Notion, or similar tools. Your stories will be so precise that engineers can size them without follow-up questions, yet so compelling that designers feel inspired to craft pixel-perfect workflows.
- • Instrument product analytics pipelines (Mixpanel, Amplitude, or Redshift) to monitor adoption, feature usage, and customer health scores. When metrics dip, you will lead root-cause analyses and rapidly iterate on UX copy, model thresholds, or training data to reverse the trend.
- • Serve as the primary voice of the customer in quarterly business reviews and user conferences. Translate real-time feedback into roadmap adjustments, ensuring our roadmap evolves faster than competitors’ while maintaining technical debt at sustainable levels.
- • Mentor junior PMs and associate product managers, fostering a culture of hypothesis-driven development, ruthless prioritization, and customer empathy. You will run internal “lunch-and-learn” sessions on topics such as causal inference in healthcare ML or the economics of DRG reimbursement.
- • Collaborate with sales and customer success to craft ROI models and case studies that accelerate enterprise deal cycles. You will join late-stage sales calls to articulate how our latest model release reduces query fatigue by 30 %, turning skeptical CMIOs into enthusiastic champions.
- • Stay ahead of the regulatory and competitive landscape—CMS reimbursement changes, new ICD-10 codes, or emerging players in clinical NLP—and translate these signals into strategic pivots that keep SmarterDx at the forefront of AI-enabled revenue-cycle management.
Skills & Technologies
About SmarterDx, Inc.
SmarterDx provides AI-powered clinical documentation integrity software that retrospectively reviews inpatient EHR data to identify missed diagnoses, quantify clinical quality gaps, and optimize revenue integrity for hospitals and health systems. Its machine-learning models surface overlooked conditions, prioritize cases for review, and feed corrected coding back to billing and quality teams, aiming to improve data accuracy, quality scores, and appropriate reimbursement without adding clinician workload.
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