Product Manager with 4+ years of experience delivering customer-centric digital products across healthcare technology and consulting environments. Specialized in driving product strategy, roadmap planning, and data-driven decision-making through advanced analytics and AI/ML integration. Expert in translating complex business requirements into actionable engineering specifications, with hands-on experience in Generative AI, SQL, product analytics, and healthcare digital platforms.

Currently pursuing MS in Information Systems at Syracuse University while bringing deep expertise in patient engagement platforms, member self-service solutions, and provider portals to transform healthcare delivery through technology.

mindmap
  root((Product<br/>Management))
    Strategy
      Product Discovery
      Market Research
      Competitive Analysis
      Voice of Customer
      Strategic Planning
    Execution
      Agile Scrum
      Sprint Planning
      PRDs
      User Stories
      Backlog Management
    Analytics
      Product Analytics
      A/B Testing
      KPI Dashboards
      Funnel Analysis
      Cohort Analysis
    Technology
      Generative AI
      Azure OpenAI
      RAG
      REST APIs
      SQL
    Stakeholders
      Cross functional
      Executive Comms
      Customer Research
      Change Management

timeline
    title Career Progression
    section Healthcare Tech
        Mar 2026 : Product Manager
                 : Humana
                 : AI Member Support
        Nov 2025 : Business Analyst Lead
                 : Upstate Family Healthcare
                 : Patient Portal UX
    section Product Growth
        May 2021 : Product Manager
                 : CitiusTech
                 : Started as BA
                 : Grew to PM role
                 : Healthcare Platforms
    section Education
        Aug 2024 : MS Information Systems
                 : Syracuse University
        Jul 2018 : BTech IT
                 : Mumbai University

graph TB
    subgraph Frontend["🎨 Patient/Member Experience"]
        A[Member Portal]
        B[Provider Portal]
        C[Patient Self-Service]
    end
    
    subgraph Gateway["🔐 API Gateway Layer"]
        D[Authentication]
        E[Authorization]
        F[Rate Limiting]
    end
    
    subgraph Services["⚙️ Core Services"]
        G[AI Assistant Service]
        H[Member Data Service]
        I[Analytics Service]
        J[Engagement Service]
    end
    
    subgraph AI["🤖 AI/ML Layer"]
        K[OpenAI APIs]
        L[Azure OpenAI]
        M[RAG System]
    end
    
    subgraph Data["💾 Data Layer"]
        N[(SQL Database)]
        O[(Analytics DB)]
        P[Adobe Analytics]
        Q[Power BI]
    end
    
    A --> D
    B --> D
    C --> D
    D --> E
    E --> F
    F --> G
    F --> H
    F --> I
    F --> J
    G --> K
    G --> L
    G --> M
    H --> N
    I --> O
    I --> P
    I --> Q

sequenceDiagram
    participant S as Stakeholders
    participant PM as Product Manager
    participant E as Engineering
    participant Q as QA
    participant U as Users
    
    S->>+PM: Business Requirements
    PM->>PM: Product Discovery
    PM->>PM: Create PRD
    PM->>+E: User Stories + Acceptance Criteria
    E->>E: Sprint Planning
    E->>E: Development
    E->>-PM: Feature Complete
    PM->>+Q: UAT Coordination
    Q->>Q: Testing
    Q->>-PM: QA Sign-off
    PM->>PM: Release Planning
    PM->>+U: Feature Launch
    U->>-PM: User Feedback
    PM->>PM: Product Analytics
    PM->>S: Performance Metrics

Role: Product Manager | Duration: Mar 2026 - Present

Impact:

• Defined user stories and acceptance criteria for AI-assisted member support features
• Translated Medicare Advantage business requirements into engineering-ready specifications
• Conducted product discovery through stakeholder partnerships and member feedback analysis
• Analyzed member portal engagement trends to monitor feature adoption

Technologies: OpenAI APIs, Azure OpenAI, RAG, REST APIs, SQL, Power BI, Adobe Analytics, Jira, Agile Scrum

Role: Business Analyst, Team Lead | Duration: Nov 2025 - Feb 2026

Impact:

• Led 7-member consulting team to gather business requirements through stakeholder workshops
• Redesigned patient onboarding and authentication experience, simplifying information architecture
• Implemented Google Analytics to monitor user behavior, page engagement, and navigation paths
• Designed experimentation framework for personalized patient landing pages with A/B testing
• Recommended streamlined single-page patient intake workflow based on usability testing

Technologies: UX Research, Google Analytics, A/B Testing, Customer Journey Mapping

Role: Product Manager (Promoted from Business Analyst) | Duration: May 2021 - Jul 2024

Impact:

• Progressed from Business Analyst to Product Manager, taking ownership of product roadmap planning
• Owned product backlog by translating business requirements into PRDs, user stories, and acceptance criteria
• Collaborated with engineering, client integration teams, and clinical SMEs to define FHIR APIs and EHR interoperability
• Coordinated cross-functional delivery managing UAT cycles, release planning, and production readiness
• Analyzed product performance using SQL, Power BI, Tableau, Mixpanel, and Google Analytics
• Facilitated product discovery through stakeholder workshops and clinical workflow assessments

Technologies: SQL, Power BI, Tableau, Excel, Jira, Confluence, Agile Scrum, FHIR APIs, HL7, EHR, Epic, Mixpanel, Google Analytics, A/B Testing

Type: Machine Learning Project | GitHub: ds_salary_proj

Technical Highlights:

• Developed ML tool to estimate data science salaries with MAE of ~$11K
• Scraped 1000+ job descriptions from Glassdoor using Python and Selenium
• Engineered features from job descriptions to quantify value of Python, Excel, AWS, and Spark
• Optimized Linear, Lasso, and Random Forest Regressors using GridSearchCV
• Built client-facing API using Flask for production deployment

Technologies: Python, Pandas, NumPy, Scikit-learn, Selenium, Flask, Machine Learning

Type: E-commerce Analytics | GitHub: online-grocery-retail

Technical Highlights:

• Analyzed 200,000 user online retail data with comprehensive feature engineering
• Extracted important features using LightGBM and XGBoost feature importance
• Segmented customers based on purchase patterns using K-Means clustering
• Achieved 38.09% prediction accuracy for product purchase prediction
• Built recommendation model using Association Rule Mining (Apriori Algorithm)

Technologies: Python, XGBoost, LightGBM, K-Means, Apriori Algorithm, Jupyter Notebook

Type: Cybersecurity AI | GitHub: enhanced-anomaly-detection-system