Dashboard / Candidate
Prashant Kumar
Applied for Product Manager · pm_06_prashant_kumar.pdf
Private: squad_6@pg27.mesaschool.co · +91 99001 43278
Applied-role score
45/100#16 of 34 PM applicants
PM score
45/100SPM score
32/100Current status
AnalysedBelow shortlist (AI suggestion)
Your decision
1
Why they fit
- Problem -> Change -> Result 25%
The candidate provides a clear example of analyzing support tickets to identify UX friction, executing a settings redesign, and achieving an 18% reduction in support volume. The action is directly linked to a concrete, measurable outcome.
- Gets to the Source Before Designing the Fix 15%
The candidate demonstrates firsthand discovery by interviewing 8 freight forwarding sales managers to build a domain-specific spec, as well as conducting 6 weeks of discovery with logistics procurement teams. Evidence that an initial design or assumption changed as a result of what was uncovered is missing.
2
Where they fall short
- Carries Ambiguous Problems Through to Closure −20 pts
Evidence is not present. The CV describes structured product development and feature launches, but does not provide an example of navigating an ambiguous escalation, operational failure, or cross-functional issue to full resolution.
- Evidence-Led Decisions −12 pts
The CV shows evidence being used to inform a redesign based on ticket analysis. However, it lacks examples of decisions being altered, halted, or reprioritized away from an initial direction based on data or post-mortems.
- Builds Repeatable Ways of Working −12 pts
The candidate introduced OKR-based roadmap planning and post-launch metric tracking at their company. However, there is no evidence demonstrating that peers or other squads subsequently adopted or reused these methods.
| Criterion | Weight | Score | Weighted | AI reasoning | Evidence from CV |
|---|---|---|---|---|---|
| Problem -> Change -> Result | 25% | 4/5 | 20.0/25 | The candidate provides a clear example of analyzing support tickets to identify UX friction, executing a settings redesign, and achieving an 18% reduction in support volume. The action is directly linked to a concrete, measurable outcome. |
|
| Evidence-Led Decisions | 20% | 2/5 | 8.0/20 | The CV shows evidence being used to inform a redesign based on ticket analysis. However, it lacks examples of decisions being altered, halted, or reprioritized away from an initial direction based on data or post-mortems. |
|
| Builds Repeatable Ways of Working | 20% | 2/5 | 8.0/20 | The candidate introduced OKR-based roadmap planning and post-launch metric tracking at their company. However, there is no evidence demonstrating that peers or other squads subsequently adopted or reused these methods. |
|
| Carries Ambiguous Problems Through to Closure | 20% | 0/5 | 0.0/20 | Evidence is not present. The CV describes structured product development and feature launches, but does not provide an example of navigating an ambiguous escalation, operational failure, or cross-functional issue to full resolution. | Evidence not present in the CV. |
| Gets to the Source Before Designing the Fix | 15% | 3/5 | 9.0/15 | The candidate demonstrates firsthand discovery by interviewing 8 freight forwarding sales managers to build a domain-specific spec, as well as conducting 6 weeks of discovery with logistics procurement teams. Evidence that an initial design or assumption changed as a result of what was uncovered is missing. |
|
| Total | 100% | 45 | The candidate shows clear competence in customer discovery and linking product adjustments to measurable outcomes, such as reducing support volume through ticket analysis. However, there is no evidence of managing messy or ambiguous escalations through to closure, and new processes introduced lack proof of broader team adoption. Overall, the CV demonstrates solid standard PM delivery but limited proof against the rubric's ambiguity and repeatable-practice criteria. | ||
Scoring scale
- 0: No evidence in the CV
- 1: Indirect or participation-only evidence
- 2: Partial: some elements of the description, weak link to outcome
- 3: One clear example matching the description
- 4: Clear example with a visible result, candidate's own action linked to it
- 5: Repeated, strong examples fully matching the description, with results
Weighted contribution = score ÷ 5 × weight. Every quote is checked against the CV text; if none can be verified, the score is capped at 1.
4
Evidence
Verified CV quotes used (PM rubric)
- “Analysed 2,000+ support tickets to identify top UX friction points; resulting insights drove a settings redesign that reduced support volume by 18%” · Problem -> Change -> Result
- “Analysed 2,000+ support tickets to identify top UX friction points; resulting insights drove a settings redesign that reduced support volume by 18%” · Evidence-Led Decisions
- “Introduced OKR-based roadmap planning; first PM at the company to measure feature success at 30, 60, and 90 days post-launch” · Builds Repeatable Ways of Working
- “Led discovery for a new logistics vertical pilot — interviewed 8 freight forwarding sales managers; built the feature spec that became the first domain-specific product offering” · Gets to the Source Before Designing the Fix
- “Led a supply chain vendor management feature that required 6 weeks of discovery with logistics procurement teams at 4 manufacturing companies — closest I have come to understanding how freight and vendor data flows in a real ops environment” · Gets to the Source Before Designing the Fix
Outcomes stated in the CV
- Shipped 14 features over 2 years with 85%+ adoption tracked at the 90-day mark at WorkflowAI Technologies
- Reduced support volume by 18% following settings redesign informed by analysis of 2,000+ support tickets at Salesken AI
- Built feature spec for the first domain-specific product offering for a logistics vertical pilot at Salesken AI
Anonymised CV (exactly what the AI saw)
[CANDIDATE] Product Manager [PHONE REDACTED] · [EMAIL REDACTED] · Bengaluru · [PROFILE URL REDACTED] Professional Summary Product Manager with 4 years of experience in B2B SaaS — CRM, workflow automation, and analytics products. Strong in discovery, PRD authoring, and cross-functional execution. MBA from TAPMI. Led a supply chain automation project during an engagement that gave me exposure to how logistics firms manage their data. Looking to apply product skills to a more operationally complex domain. Work Experience Product Manager WorkflowAI Technologies (B2B SaaS, 90 employees), Bengaluru · Aug 2022 – Present — Owns the workflow automation and integration module — 200+ enterprise clients across manufacturing, retail, and logistics verticals — Shipped 14 features over 2 years; maintained 85%+ adoption on launched features tracked at 90-day mark — Led a supply chain vendor management feature that required 6 weeks of discovery with logistics procurement teams at 4 manufacturing companies — closest I have come to understanding how freight and vendor data flows in a real ops environment — Manages a 3-person squad: 2 engineers and 1 designer; reports to VP Product — Introduced OKR-based roadmap planning; first PM at the company to measure feature success at 30, 60, and 90 days post-launch Associate Product Manager Salesken AI (Conversation Intelligence SaaS), Bengaluru · Jul 2020 – Jul 2022 — Supported PM on the call analytics and coaching dashboard — owned 3 features from spec to release in 2 years — Led discovery for a new logistics vertical pilot — interviewed 8 freight forwarding sales managers; built the feature spec that became the first domain-specific product offering — Analysed 2,000+ support tickets to identify top UX friction points; resulting insights drove a settings redesign that reduced support volume by 18% Education MBA, Marketing & Product Management · T.A. Pai Management Institute (TAPMI), Manipal · 2018–2020 B.Tech, Computer Science · NIT Surathkal · 2014–2018 · CGPA 7.9/10 Skills & Certifications Product roadmapping · PRD writing · Agile/Scrum · JIRA · Figma · SQL · Amplitude · Mixpanel · Salesforce Reforge Member (2023–24) · Product School PM Certification (2021)
Redacted: name ×1, email ×1, phone ×1, profile url ×1
5
Interview brief
Briefs are written when you shortlist a candidate.
6
Email draft
An offer or rejection draft is prepared once you make a decision. Nothing is ever sent without your approval.
Audit trail (2)
- 9/29/2026, 11:02:06 AMsystemanalysis completed{"PM_score":45,"SPM_score":32}
- 9/29/2026, 11:01:25 AMsystemcv ingested{"file":"pm_06_prashant_kumar.pdf","role":"PM","redactions":{"name":1,"email":1,"phone":1,"profile_url":1},"name_detected":true}