Tech Product Manager

Project description

This role involves managing IT projects within a healthcare environment, ensuring timely delivery, compliance, and stakeholder alignment. The candidate will lead cross-functional teams, coordinate with onshore clients, and manage multiple tasks simultaneously. Strong communication, organizational skills, and familiarity with project management tools and IT infrastructure are essential.

Responsibilities

• Lead and manage IT projects from initiation to closure, ensuring adherence to timelines and quality standards. Collaborate with cross-functional teams and stakeholders to achieve project objectives. Communicate effectively with onshore clients and maintain strong client relationships. Ensure compliance with IT infrastructure standards and SDLC processes. Handle multiple tasks and prioritize effectively in a fast-paced environment. Maintain a strong focus on customer service and teamwork. Own and drive the product roadmap for healthcare technology products, including data, analytics, and AI capabilities Define requirements for dashboards, KPIs, analytics, and AI-enabled solutions Ensure solutions meet healthcare regulatory, compliance, responsible AI, and patient safety requirements.

SKILLS

Must have

• 5+ years in technology product or platform product roles Demonstrated experience delivering healthcare technology products Direct exposure to healthcare data, analytics, or AI enabled solutions Experience operating in regulated environments (HIPAA, PHI, healthcare compliance) Proven ability to lead complex healthcare initiatives through ambiguity and change Strong understanding of healthcare operations and workflows, including payer, provider, and care management contexts Familiarity with healthcare domains such as: Utilization Management (UM) Care Management / Case Management Prior Authorization Ability to translate clinical and operational workflows into scalable technology solutions Deep appreciation for regulatory, compliance, and patient safety considerations in product decisions Outcome driven product mindset grounded in healthcare realities and AI /data first thinking Ability to identify where data, analytics, and AI improve healthcare outcomes or reduce administrative burden Roadmap ownership integrating: Core healthcare capabilities Data and interoperability foundations Analytics and AI enablement Prioritization informed by clinical impact, operational value, feasibility, and risk Strong problem framing focused on insight generation, automation, and scale Working understanding of: Healthcare data domains (member, provider, clinical, authorization, outcomes) Data flows across payer, provider, and partner systems Ability to partner with data engineering, analytics, and AI teams to: Enable reliable healthcare analytics and reporting Support decision making through insights and AI assisted summarization Experience defining requirements for: Operational and clinical dashboards KPIs tied to quality, efficiency, cost, and experience AI enabled analytics (summarization, prioritization, decision support) Awareness of data quality, lineage, interoperability, and adoption challenges in healthcare Understanding of AI use cases relevant to healthcare: Clinical and operational summarization Decision support (human in the loop) Workflow automation and prioritization Ability to balance innovation with: Responsible AI practices Explainability and auditability Regulatory and ethical considerations Experience guiding AI from pilot ? production ? scaled adoption in healthcare settings Knowledge of modern architectures supporting healthcare data and AI: APIs, microservices, cloud platforms Data platforms, interoperability layers, and integration patterns Ability to evaluate build vs buy vs reuse with healthcare constraints in mind Comfort navigating legacy systems, technical debt, and complex integrations common in healthcare ecosystems Agile / SAFe execution with emphasis on progress, readiness, and adoption Strong dependency and risk management across: Product teams Data pipelines Analytics and AI platforms Release planning that includes: Data readiness and validation Analytics accuracy AI model monitoring and compliance checks Trusted partner to business, clinical, and operations leaders Clear articulation of tradeoffs, risks, and value realization

Nice to have

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