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-based engineering and specialist resources, AI-assisted delivery, healthcare-oriented SaaS implementation, sprint execution, RAID/dependency tracking, UAT, deployment, training, and executive reporting.

Scoring Notes
Scores use only the submitted evaluation responses and supplied JD/project context.
Missing attachments or referenced samples not included in the provided content were treated as missing evidence, not as negative facts.
AI assistance or AI exposure was not treated as disqualifying; generic or polished responses are flagged only as interview probes for ownership and practical depth.

Scoring Parameters
- Enterprise delivery and domain fit (20): Relevant senior ownership of complex enterprise, SaaS, regulated, healthcare, data-heavy, or B2B software delivery with clear role, team, duration, and delivery model evidence.
- US stakeholder and executive communication (15): Direct work with US-based founders, executives, product owners, or client stakeholders, including meetings, escalations, decisions, time-zone overlap, and written follow-up.
- Execution governance and artifacts (20): Personal ownership of WBS, sprint plans, milestone trackers, RAID logs, decision logs, UAT/defect/change trackers, resource plans, and executive-ready status reporting.
- Healthcare SaaS technical PM depth (20): Ability to manage risks and dependencies across APIs, cloud environments, security/privacy, access control, audit logging, QA, UAT, data migration, deployment, and training.
- Cross-functional coordination and problem solving (15): Ability to coordinate fractional/vendor resources, clarify ownership, manage risks, communicate bad news, handle delayed architecture, and separate defects from scope changes.
- Fractional fit and AI-assisted delivery readiness (10): Fit for 10-hour/week India-based fractional model, US overlap, start timing, structured follow-through, and practical governance of AI-assisted delivery.

Candidate Ranking
- Ashwini: 83/100 - Possible shortlist
- Sajal Sharma: 81/100 - Possible shortlist
- Sagar Bhalla: 76/100 - Needs careful review

Ashwini
Strong practical evidence across artifacts, healthcare CRM workflows, US stakeholder communication, sprint/UAT discipline, security-aware delivery, and fractional availability. Main limitation is that the two requested enterprise project examples and sample status report are referenced as attachments but not present in the supplied content.
Enterprise delivery and domain fit: 7/10 - Provides relevant examples across healthcare CRM, AI contract management, insurance ticketing, and quotation management, but the requested two-project attachment with team/duration/delivery-model detail was not supplied.
US stakeholder and executive communication: 9/10 - Current direct work with US founder/executive team, weekly reviews, roadmap/planning sessions, decision meetings, written follow-up, and escalations are clearly described.
Execution governance and artifacts: 8/10 - Lists broad artifact ownership and explains use of WBS, sprint plans, RAID, decision registers, resource plans, UAT, defect/change trackers, release plans, and executive reports; sample report attachment was not available.
Healthcare SaaS technical PM depth: 9/10 - Strong risk/dependency coverage for architecture, APIs, cloud readiness, access control, audit logging, migration, QA/UAT, deployment, training, and healthcare CRM data controls.
Cross-functional coordination and problem solving: 9/10 - Gives concrete examples of vendor coordination, SSO dependency escalation, delayed architecture response, and UAT change-control handling.
Fractional fit and AI-assisted delivery readiness: 8/10 - Commits 12-15 hours weekly with 8-11 PM IST overlap and start within 2 business days; AI experience is more AI-enabled product delivery than managing AI code-generation workflows.
Strong points:
- Clear fit with US-facing fractional delivery model and structured written follow-up.
- Strong healthcare-adjacent SaaS awareness, including RBAC, masking, auditability, API controls, migration reconciliation, QA/UAT, and penetration testing.
- Good practical delivery judgment in escalation, sprint acceptance, and UAT scope-control examples.
Weak points:
- Requested two enterprise project examples were referenced as an attachment but not included in the supplied content.
- Executive status report sample was referenced but not included, so quality of actual artifact cannot be verified here.
- AI-assisted development governance experience is narrower than the project context, which includes AI agents across documentation, cloud, development, testing, review, and training.
Probe further:
- Ask her to walk through one complete enterprise project from the missing attachment: domain, team size, duration, delivery model, exact ownership, artifacts maintained, and final outcome.
- Ask for a live walkthrough of a redacted weekly executive status report, including how she decided RAG status and escalations.
- Ask how she would govern AI agents producing code, tests, security review notes, and training materials when she is not the technical lead.

Sajal Sharma
Strong senior enterprise delivery and governance background, with large regulated government and financial services programs, strong artifact/process coverage, and relevant risk framing. Healthcare-specific implementation evidence is weaker than Ashwini and Sagar, and several answers are polished and broad, so interview should test hands-on ownership.
Enterprise delivery and domain fit: 9/10 - Provides two substantial enterprise programs with domains, roles, durations, delivery models, team scale, sensitive data, integrations, UAT, release planning, and governance responsibilities.
US stakeholder and executive communication: 8/10 - Describes extensive US stakeholder and executive governance experience, including steering reviews, milestone planning, risk escalations, release approvals, and offshore follow-up.
Execution governance and artifacts: 9/10 - Strong list of personally owned artifacts and clear executive status report structure with RAG, risks, dependencies, decisions, resources, and escalations.
Healthcare SaaS technical PM depth: 7/10 - Good coverage of architecture, environments, access, audit logging, APIs, migration, QA/UAT, and training, but direct healthcare/HIPAA evidence is limited to assessments; strongest domain evidence is government and financial services.
Cross-functional coordination and problem solving: 8/10 - Strong governance approach using RACI, RAID, action logs, decision logs, escalation paths, and change-control triage, though examples are relatively high-level.
Fractional fit and AI-assisted delivery readiness: 7/10 - Shows AI governance principles and recent AI consulting exposure, but the provided content is truncated before availability/start confirmation, so fractional fit cannot be fully verified.
Strong points:
- Strong senior-scale enterprise governance experience with large multi-vendor and global delivery programs.
- Clear understanding of executive reporting, RAID, release governance, UAT, change control, and operational readiness.
- Good regulated-domain instincts around auditability, controlled releases, traceability, and stakeholder approvals.
Weak points:
- Direct healthcare implementation evidence is limited compared with the role’s healthcare SaaS context.
- Availability and start-date answer is not visible in the supplied content, so fit for the 10-hour/week model is incomplete.
- Some responses are broad and highly polished; practical ownership depth should be verified.
Probe further:
- Ask him to walk through the Defence Pension program artifacts he personally maintained, especially RAID, decision log, UAT tracker, and executive report examples.
- Ask for a concrete healthcare-oriented scenario: how he would handle PHI/PII boundaries, access controls, audit logging, and data migration validation in the first two sprints.
- Ask him to reproduce a one-page weekly status report live from a hypothetical delayed architecture/security review scenario.

Sagar Bhalla
Good apparent match on healthcare EHR implementation, API/interface awareness, Jira use, AI-assisted delivery controls, and strong availability. Responses are concise but often generic, with missing specifics on duration, exact team scale, client/stakeholder detail, and actual artifacts, so shortlist only if interview confirms ownership and depth.
Enterprise delivery and domain fit: 7/10 - Includes EPIC-based EHR and AI/blockchain LMS examples with relevant responsibilities, but lacks duration, exact role depth, team size, delivery outcome, and more detailed enterprise evidence.
US stakeholder and executive communication: 7/10 - States regular US founder/product owner/stakeholder engagement, demos, status meetings, overlap, and written follow-ups, but provides few specific decisions or escalations.
Execution governance and artifacts: 7/10 - Lists relevant artifacts and gives standard status report and sprint process descriptions; evidence is adequate but not deeply grounded in a specific artifact sample.
Healthcare SaaS technical PM depth: 8/10 - Healthcare EHR experience plus good first-two-week risk coverage across architecture, cloud, APIs, permissions, test environments, data migration, UAT, training, access controls, audit logging, and data handling.
Cross-functional coordination and problem solving: 7/10 - Shows reasonable handling of vendor coordination, RACI/action registers, delayed integration risk, architecture delay, and UAT scope triage, but examples remain high-level.
Fractional fit and AI-assisted delivery readiness: 8/10 - Commits 25-30 hours weekly, 3-4 hours US overlap, immediate start, and describes AI review gates, code reviews, QA validation, and acceptance testing.
Strong points:
- Most direct healthcare system example among the visible responses through an EPIC-based EHR implementation.
- Comfortable with APIs, access control, audit requirements, UAT, deployment readiness, Jira configuration, and AI-assisted delivery review gates.
- Availability exceeds the requested 10 hours per week and includes US overlap.
Weak points:
- Responses are sparse compared with stronger candidates and lack dates, team scale, delivery outcomes, and exact personal ownership details.
- US stakeholder examples are generalized and do not show specific executive decisions or escalations handled.
- Artifact evidence is described rather than demonstrated; no redacted sample or detailed structure beyond standard sections.
Probe further:
- Ask him to walk through the EPIC-based EHR project in detail: duration, team size, interfaces, his exact authority, artifacts owned, risks escalated, and final implementation outcome.
- Ask him to show how he configured Jira for RAID, dependencies, UAT defects, and executive reporting, or recreate a small board/reporting structure live.
- Ask for a specific example where AI-generated code or test output was rejected or corrected, and how accountability was maintained.