Skills & Qualifications

    • Skilled in applying Agile and Scrum frameworks to manage full product lifecycles, from ideation through delivery and post-launch support.

    • Expert in backlog refinement, epic/story management, and sprint planning to ensure efficient development cycles and clear prioritization.

    • Translates complex clinical and technical requirements into actionable features and user stories that engineering and data science teams can execute against.

    • Facilitates daily standups, sprint reviews, retrospectives, and release planning to drive team accountability and continuous improvement.

    • Uses data-driven prioritization to balance stakeholder demands with technical feasibility, optimizing product impact and delivery velocity.

    • Champions iterative delivery and rapid experimentation, ensuring features align with both customer needs and long-term product strategy.

    • Collaborates with cross-functional stakeholders to embed user feedback into iterative design cycles, enhancing usability and adoption.

    • Owns creation and maintenance of Product Requirement Documents (PRDs), quality assurance frameworks, and release cadences for multiple concurrent products.

    • Builds scalable processes and workflows that support product consistency, efficiency, and repeatability across teams and domains.

    • Establishes KPIs and success metrics to measure customer experience, market adoption, and business impact, with dashboards to track performance over time.

    • Creates operational playbooks, workflow maps, and process guidelines to support teams across engineering, clinical terminology, data science, and implementation.

    • Partners with support, marketing, and commercialization teams to ensure smooth product rollouts, issue resolution, and customer success.

    • Introduces tools and practices that improve transparency and communication, such as RACI frameworks, roadmaps, and release documentation.

    • Builds cross-functional alignment through structured communication channels, ensuring engineering execution stays tied to business objectives.

    • Develops go-to-market strategies that align product launches with organizational goals, market opportunities, and customer needs.

    • Defines product positioning, pricing strategies, and adoption roadmaps in collaboration with commercialization, marketing, and sales teams.

    • Conducts market research and competitive analysis to guide product direction and identify opportunities for differentiation.

    • Creates stakeholder presentations, demos, and training sessions that drive product awareness, customer buy-in, and organizational alignment.

    • Partners with business development teams to evaluate potential partnerships, integrations, and joint ventures.

    • Crafts customer journeys, use cases, and adoption scenarios that highlight business value and support sales enablement.

    • Ensures compliance with regulatory and payer frameworks (e.g., CMS, LCD/NCD guidelines, HIPAA) during go-to-market planning.

    • Leads enterprise-level programs spanning multiple teams, products, and business units while ensuring scope, budget, and timelines are met.

    • Builds program-level governance structures, including RACI models, dependency mapping, and risk tracking, to ensure accountability and clarity.

    • Coordinates activities across engineering, data science, clinical terminology, implementation, support, and commercialization teams.

    • Develops end-to-end workflows for AI-driven products, balancing innovation with operational efficiency and compliance.

    • Manages stakeholder communication at all levels, from executive sponsors to frontline product users, aligning project milestones with organizational strategy.

    • Creates program metrics and success measures to evaluate adoption, efficiency gains, and business outcomes.

    • Mentors new hires and associates, providing onboarding, training, and professional development to strengthen team capability.

    • Natural Language Processing (NLP): Experienced in managing NLP pipelines, annotation tools, and concept extraction systems for healthcare documentation. Led initiatives to improve accuracy, optimize data quality workflows, and integrate NLP into commercial-facing products.

    • Large Language Models (LLMs): Directed the development and validation of explainable LLM agents across problem, procedure, medication, and lab domains. Defined scoring frameworks, prompts, and workflows to improve confidence, precision, and interoperability.

    • Semantic Knowledge Graphs (SKG): Led the design and validation of clinical relationships across medications, conditions, labs, and procedures. Defined scalable processes for curation, validation, and ontology management to enable AI-driven decision support.

    • Clinical Data Interoperability: Deep expertise with HL7, FHIR, OMOP, and standard terminologies (SNOMED CT, RxNorm, LOINC, ICD-10-CM, CPT). Ensured product compliance with interoperability standards while enabling scalable integrations.

    • Healthcare Operations & Reimbursement: Applied coding and compliance expertise to align clinical AI products with payer requirements, coverage determinations, and reimbursement policies. Strengthened solutions for prior authorization, coding accuracy, and insurance workflows.

    • AI-Driven Product Innovation: Researched and implemented use cases for generative AI, ambient transcription, and digital twin technology. Defined product requirements and adoption strategies that balanced cutting-edge AI with safety, compliance, and usability.

    • Enterprise-Scale Program Tools: Hands-on experience with Jira, Confluence, GitLab, FusionAuth, and data governance platforms to support engineering, release management, and collaboration at scale.