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Candidate Evaluation Scoring Report: Fractional Technical Project Manager, Enterprise Healthcare Software Implementation
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Candidate Evaluation Scoring Report: Fractional Technical Project Manager, Enterprise Healthcare Software Implementation Senior India-based fractional Technical Project Manager for Pikes Peak PPRS Phase 1, coordinating US executive stakeholders, India delivery teams, fractional specialists, AI-assisted delivery, architecture, security, QA, UAT, migration, training, deployment, and disciplined project execution artifacts. Scoring Notes Scores are evidence-based from submitted evaluations only; missing or sparse evidence is scored conservatively. Overall scores use weighted 0-10 criterion scores converted to 100 points. Polished or generic responses are not treated as misconduct, but interviewers should verify ownership and practical depth through artifact walkthroughs and live scenarios. Scoring Parameters - Relevant enterprise and healthcare delivery experience (20): Fit to complex B2B, SaaS, healthcare, regulated-domain, data-heavy, migration, and enterprise implementation work. - Execution artifacts and governance discipline (20): Evidence of personally maintaining WBS, sprint plans, milestone trackers, RAID, decision logs, UAT trackers, change trackers, and executive reports. - Technical PM depth for healthcare SaaS (20): Ability to manage risks and dependencies across architecture, APIs, cloud, security/privacy, QA, UAT, data migration, deployment, and training. - US stakeholder and executive communication (15): Experience running structured meetings, written follow-up, escalation, time-zone overlap, and executive-ready communication with US stakeholders. - Ownership, coordination, and problem solving (15): Evidence of driving decisions, vendor or fractional resource coordination, bad-news communication, ambiguous delivery recovery, UAT triage, and signoff. - Tools, AI-assisted delivery governance, and availability fit (10): Hands-on tooling depth, AI-assisted delivery controls where relevant, and fit for a fractional India-based role with US overlap. Candidate Ranking - Poornima: 90/100 - Strong shortlist - Anirban Saha: 88/100 - Strong shortlist - Rahul Walia: 79/100 - Possible shortlist - Khakshan Anjum: 74/100 - Possible shortlist - Hemish Sagar: 58/100 - Needs careful review Poornima Best role fit based on direct US healthcare project evidence, claims/data warehouse context, detailed governance artifacts, strong healthcare SaaS risk coverage, and credible UAT/release practices. Some later response content was truncated in the supplied material, so interview should verify vendor coordination and availability details. Relevant enterprise and healthcare delivery experience: 9/10 - Describes two US healthcare projects: HPRO provider ratings and Galaxy healthcare data warehouse involving claims, insurance, provider data, large records, and distributed teams. Execution artifacts and governance discipline: 9/10 - Gives detailed ownership of WBS, sprint plan, release tracker, RAID, decision log, resource plan, UAT tracker, defect/change tracker, and executive status report. Technical PM depth for healthcare SaaS: 9/10 - Strong coverage of architecture, APIs, environments, security, audit logging, data migration, reconciliation, UAT ownership, deployment, training, and client decisions. US stakeholder and executive communication: 9/10 - Claims around 18 years working with US stakeholders, steering meetings, roadmap discussions, release readiness, incident reviews, and written follow-ups. Ownership, coordination, and problem solving: 9/10 - Shows practical sprint acceptance, UAT signoff, risk/dependency management, release planning, and production escalation ownership. Tools, AI-assisted delivery governance, and availability fit: 9/10 - Strong Jira/Confluence configuration detail and explicit AI governance controls; availability response was not visible in the supplied truncated content. Strong points: - Direct healthcare implementation evidence, including provider ratings, data warehouse, claims, insurance, provider data, and sensitive data handling. - Most complete healthcare SaaS risk framework among candidates. - Strong artifact ownership and executive report example with RAG, milestones, risks, issues, decisions, and owners. Weak points: - Some supplied response content is truncated, leaving incomplete evidence for vendor/fractional coordination and availability. - Writing is polished and broad in places, so ownership of specific artifacts should be verified. Probe further: - Ask her to walk through the HPRO weekly executive status report and explain which fields she personally maintained. - Ask for a live example of separating UAT defects from scope changes in the HPRO or Galaxy project. - Ask her to map the first two-week RAID log for Pikes Peak PPRS using architecture, API, security, migration, UAT, and training dependencies. - Confirm weekly capacity, start date, and exact US overlap window. Anirban Saha Very strong structured PM profile with regulated-domain experience, mature governance language, US stakeholder cadence, detailed artifacts, and strong AI-assisted delivery controls. Direct healthcare experience is adjacent rather than explicit, but the response is deep and practical. Relevant enterprise and healthcare delivery experience: 8/10 - Strong regulated compliance and enterprise migration experience at PwC and BT, but healthcare is explicitly described as adjacent rather than direct. Execution artifacts and governance discipline: 10/10 - Detailed ownership of WBS, RAID, sprint plans, milestone tracker, executive reporting, UAT tracker, decision log, and resource plan with clear usage. Technical PM depth for healthcare SaaS: 9/10 - Excellent first-two-week risk coverage across architecture, environments, security, audit logging, APIs, migration validation, UAT ownership, and client decisions. US stakeholder and executive communication: 9/10 - Specific EST/PST overlap, steering meetings, written follow-up within 2 hours, escalation example, and senior stakeholder reporting. Ownership, coordination, and problem solving: 9/10 - Strong examples for multi-vendor coordination, bad-news escalation, 48-hour risk recovery, and late UAT triage. Tools, AI-assisted delivery governance, and availability fit: 9/10 - Deep Jira/Confluence/MS Project/Power BI ownership, explicit AI usage log and human review gates, and 20-25 hours/week with US overlap. Strong points: - Highly structured delivery operating model with strong artifact discipline. - Clear US stakeholder communication cadence and escalation handling. - Strong AI-assisted delivery governance: review gates, human ownership, usage logging, and acceptance steps. Weak points: - No direct healthcare implementation claim; experience is regulated/compliance-adjacent. - Very polished response should be validated through artifact walkthroughs and practical scenario testing. Probe further: - Ask him to reproduce a one-page executive status report live for the Pikes Peak project. - Ask him to walk through one PwC sprint where he defined acceptance criteria and handled UAT signoff. - Probe healthcare-specific gaps: HIPAA-style access controls, audit logging, PHI/PII handling, and data migration validation. - Ask what he personally owned versus what technical leads owned in the AI-assisted delivery governance process. Rahul Walia Strong claimed enterprise SaaS and HIPAA-compliant healthcare SaaS fit, with good US stakeholder and tooling evidence. However, several answers remain high-level and generic, especially status report details, 48-hour plan, and UAT closure depth. Relevant enterprise and healthcare delivery experience: 9/10 - Claims direct HIPAA-compliant healthcare SaaS delivery and a large regulated public transportation SaaS platform with 25-30+ resource teams. Execution artifacts and governance discipline: 8/10 - Lists broad artifact ownership, but executive status report example is described generically rather than with concrete sample content. Technical PM depth for healthcare SaaS: 8/10 - Covers architecture, environments, cloud, APIs, HIPAA/security review, QA/UAT, deployment/rollback, and data migration, but first-two-week answer is brief. US stakeholder and executive communication: 8/10 - Describes US executives, steering committees, roadmap sessions, UAT signoff, release planning, and written logs. Ownership, coordination, and problem solving: 7/10 - Good escalation and RACI language, but scenario and UAT answers are incomplete and less specific than top candidates. Tools, AI-assisted delivery governance, and availability fit: 8/10 - Claims 12 years Jira/Confluence with configuration detail, AI review controls, and immediate 40-50 hours/week availability with US overlap. Strong points: - Direct claimed healthcare SaaS and HIPAA-compliant platform experience. - Good breadth across Jira/Confluence configuration, RAID, dashboards, and AI-assisted delivery controls. - Comfortable with US stakeholders and high weekly availability. Weak points: - Some answers are generic and do not show enough concrete artifact examples. - UAT and 48-hour problem-solving answers are partially developed and should be validated. - Availability exceeds the expected 10 hours/week model, so fractional fit and expectations should be clarified. Probe further: - Ask him to walk through the HIPAA-compliant healthcare SaaS project, including what he personally owned versus compliance/security teams. - Ask for a live sample RAID log for the first two weeks of Pikes Peak PPRS. - Ask him to separate five example UAT items into defect, clarification, and change request categories with signoff steps. - Confirm whether he is comfortable with a focused 10-hour/week fractional operating cadence despite offering 40-50 hours/week. Khakshan Anjum Good Agile PM profile with SaaS, fintech/payment, Salesforce healthcare CRM, Jira/Confluence, RAID, UAT, and stakeholder coordination. Evidence is relevant but less deep for healthcare implementation, security/privacy, AI-assisted development governance, and concrete US executive escalation examples. Relevant enterprise and healthcare delivery experience: 7/10 - Describes fintech SaaS and Salesforce healthcare CRM/customer engagement project; healthcare fit is present but not as deep as direct claims/data implementation. Execution artifacts and governance discipline: 8/10 - Lists project plan, Jira backlog, RAID, status reports, minutes, UAT/release docs, and change logs, though weekly report example refers to an attachment not included in supplied text. Technical PM depth for healthcare SaaS: 7/10 - Covers APIs, security/compliance delays, data migration, environments, QA/UAT, and cloud dependencies, but limited detail on audit logging, migration validation, and deployment readiness. US stakeholder and executive communication: 7/10 - Claims 19 years with US stakeholders and meeting facilitation, but fewer concrete examples of decisions, escalations, and written follow-up. Ownership, coordination, and problem solving: 8/10 - Provides practical RACI, workstream ownership, Jira dependency tracking, bad-news escalation, 48-hour re-sequencing, and UAT triage. Tools, AI-assisted delivery governance, and availability fit: 7/10 - Strong Jira/Confluence usage; AI answer focuses on Zapier/Make.com integration rather than AI-assisted development quality controls. Strong points: - Relevant SaaS, Salesforce healthcare CRM, payments, integration, UAT, and release management background. - Good Jira/Confluence configuration detail for sprint tracking, RAID, dependencies, and reporting. - Understands RACI, owner assignment, UAT triage, and scope change control. Weak points: - Healthcare evidence is healthcare-adjacent/CRM rather than clearly HIPAA, claims, or deep healthcare implementation. - Executive report content was not provided in the text beyond a reference to an attachment. - AI-assisted delivery evidence is weak for code generation, testing, review gates, and accountability. Probe further: - Ask her to walk through the Salesforce healthcare CRM project and explain data sensitivity, access controls, and audit requirements. - Ask her to recreate the weekly governance report sections live, including RAG, risks, decisions, owners, and dates. - Ask what she personally configured in Jira versus what the team maintained. - Probe how she would govern AI-generated code, tests, and documentation in this project. Hemish Sagar Has relevant cloud migration and some healthcare data migration exposure, including Azure and US stakeholder work, but the submitted evaluation is sparse. Many artifact examples rely on screenshots not included in supplied text, and several answers lack concrete ownership, technical depth, or executive communication detail. Relevant enterprise and healthcare delivery experience: 6/10 - Describes healthcare data migration to Azure and AT&T telecom migration, but teams are small and healthcare context is unclear. Execution artifacts and governance discipline: 5/10 - Mentions integrated plans, RAID, milestone/action trackers, and screenshots, but little textual detail is available about artifact structure or usage. Technical PM depth for healthcare SaaS: 6/10 - Identifies scope, security/privacy, QA, UAT, and acceptance risks, but misses several specifics such as audit logging, API dependencies, deployment readiness, and migration validation detail. US stakeholder and executive communication: 5/10 - Mentions AT&T stakeholders and escalation management, but lacks concrete meetings, decisions, time-zone handling, and written follow-up examples. Ownership, coordination, and problem solving: 6/10 - Shows basic owner/date/dependency tracking and UAT triage understanding, but bad-news and 48-hour scenario answers are high-level. Tools, AI-assisted delivery governance, and availability fit: 6/10 - Uses MS Project and Excel for RAID/reporting; AI answer is minimal; availability is immediate with significant hours and overlap. Strong points: - Relevant Azure cloud migration and data migration exposure. - Some healthcare-sensitive data and governance awareness. - Immediate availability and substantial stated US overlap. Weak points: - Response is sparse and depends on screenshots that are not visible in the supplied content. - Limited concrete evidence of executive-ready reporting, sprint acceptance, AI governance, and US stakeholder decision ownership. - Availability response suggests full-time capacity rather than the expected focused 10-hour fractional model. Probe further: - Ask him to show or recreate the referenced artifact tabs and explain how RAID, milestones, UAT, and decisions were maintained. - Ask for a specific AT&T escalation example with what he said, who decided, and what changed afterward. - Ask him to produce a concise weekly status report live from a sample project scenario. - Confirm comfort with a 10-hour/week fractional role rather than a full-time engagement.