Dashboard / Candidate
Rohan Sane
Applied for Product Manager · pm_10_rohan_sane.pdf
Private: squad_4@pg27.mesaschool.co · +91 93720 84315
Applied-role score
72/100#5 of 34 PM applicants
PM score
72/100SPM score
43/100Current status
AnalysedRecommend shortlist (AI suggestion)
Your decision
1
Why they fit
- Problem -> Change -> Result 25%
The candidate provides multiple strong examples where they identified a friction point, delivered a product change, and achieved quantifiable business outcomes, such as reducing onboarding time and manual intervention.
- Evidence-Led Decisions 20%
The candidate explicitly demonstrates evidence-led decision-making where experimental data directly altered product release decisions. However, detailed post-decision results for those specific altered releases are not elaborated beyond the counts.
- Gets to the Source Before Designing the Fix 15%
The CV provides repeated, strong examples of firsthand user research (interviews with brand operations managers and on-the-ground delivery partners) directly shaping subsequent product redesigns and delivering measurable results.
2
Where they fall short
- Carries Ambiguous Problems Through to Closure −16 pts
The CV shows proactive collaboration with client success to prevent escalations, but lacks any concrete example of driving a messy, multi-functional issue or escalation from identification through to post-resolution closure.
| Criterion | Weight | Score | Weighted | AI reasoning | Evidence from CV |
|---|---|---|---|---|---|
| Problem -> Change -> Result | 25% | 5/5 | 25.0/25 | The candidate provides multiple strong examples where they identified a friction point, delivered a product change, and achieved quantifiable business outcomes, such as reducing onboarding time and manual intervention. |
|
| Evidence-Led Decisions | 20% | 4/5 | 16.0/20 | The candidate explicitly demonstrates evidence-led decision-making where experimental data directly altered product release decisions. However, detailed post-decision results for those specific altered releases are not elaborated beyond the counts. |
|
| Builds Repeatable Ways of Working | 20% | 3/5 | 12.0/20 | The candidate created an A/B testing framework from scratch that was used to run multiple experiments across the team, demonstrating the creation of a repeatable tool. Evidence showing widespread, ongoing reuse by others across the organisation is limited. |
|
| Carries Ambiguous Problems Through to Closure | 20% | 1/5 | 4.0/20 | The CV shows proactive collaboration with client success to prevent escalations, but lacks any concrete example of driving a messy, multi-functional issue or escalation from identification through to post-resolution closure. |
|
| Gets to the Source Before Designing the Fix | 15% | 5/5 | 15.0/15 | The CV provides repeated, strong examples of firsthand user research (interviews with brand operations managers and on-the-ground delivery partners) directly shaping subsequent product redesigns and delivering measurable results. |
|
| Total | 100% | 72 | The candidate demonstrates exceptional strength in firsthand discovery, evidence-informed execution, and driving concrete measurable improvements across product workflows. The primary gap is a lack of explicit evidence around resolving complex, ambiguous multi-stakeholder escalations through to long-term closure. | ||
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)
- “Redesigned the exception management dashboard based on 20+ discovery interviews with brand operations managers; reduced operations team's daily manual intervention by 28%” · Problem -> Change -> Result
- “Shipped a bulk order upload and validation feature that reduced client onboarding time from 3 weeks to 4 days” · Problem -> Change -> Result
- “Led a UX research initiative with 30 delivery partners across Mumbai and Bengaluru — findings drove a navigation redesign that reduced task completion time by 22%” · Problem -> Change -> Result
- “Built the team's A/B testing framework from scratch; ran 6 experiments in the first 8 months, 3 of which changed release decisions” · Evidence-Led Decisions
- “Built the team's A/B testing framework from scratch; ran 6 experiments in the first 8 months, 3 of which changed release decisions” · Builds Repeatable Ways of Working
- “Runs regular sessions with the client success team to surface operational complaints before they become escalations” · Builds Repeatable Ways of Working
- “Runs regular sessions with the client success team to surface operational complaints before they become escalations” · Carries Ambiguous Problems Through to Closure
- “Redesigned the exception management dashboard based on 20+ discovery interviews with brand operations managers; reduced operations team's daily manual intervention by 28%” · Gets to the Source Before Designing the Fix
- “Led a UX research initiative with 30 delivery partners across Mumbai and Bengaluru — findings drove a navigation redesign that reduced task completion time by 22%” · Gets to the Source Before Designing the Fix
Outcomes stated in the CV
- Reduced operations team's daily manual intervention by 28% via exception management dashboard redesign
- Reduced client onboarding time from 3 weeks to 4 days with bulk order upload and validation feature
- Reduced delivery partner task completion time by 22% through UX research-driven navigation redesign
- Ran 6 experiments via a newly built A/B testing framework, changing 3 release decisions
Anonymised CV (exactly what the AI saw)
[CANDIDATE] Product Manager [PHONE REDACTED] · [EMAIL REDACTED] · Bengaluru · [PROFILE URL REDACTED] Professional Summary Product Manager with 3 years in B2B SaaS and marketplace products. Strong in discovery, analytics, and cross-functional execution. Worked on products for enterprise operations teams — understand how complex workflows translate into software requirements. Good at shipping and measuring. Have not worked in logistics directly but have built for operational users. Work Experience Product Manager Shadowfax Technologies (Logistics & Delivery SaaS, 500+ employees), Bengaluru · Jan 2023 – Present — PM for the client-facing order management and tracking module — 300+ brand and enterprise clients use the platform — Redesigned the exception management dashboard based on 20+ discovery interviews with brand operations managers; reduced operations team's daily manual intervention by 28% — Shipped a bulk order upload and validation feature that reduced client onboarding time from 3 weeks to 4 days — Works within a 4-person PM team; owns this module with a PM Lead above; operates independently on day-to-day decisions — Runs regular sessions with the client success team to surface operational complaints before they become escalations Associate Product Manager Shadowfax Technologies, Bengaluru · May 2021 – Dec 2022 — Supported PM on the delivery partner app — owned 4 features from PRD to release across 18 months — Led a UX research initiative with 30 delivery partners across Mumbai and Bengaluru — findings drove a navigation redesign that reduced task completion time by 22% — Built the team's A/B testing framework from scratch; ran 6 experiments in the first 8 months, 3 of which changed release decisions Education B.Tech, Computer Science · Symbiosis Institute of Technology, Pune · 2017–2021 · CGPA 8.2/10 Skills & Certifications Product management · Delivery & logistics platforms · A/B testing · JIRA · Figma · SQL · Amplitude · Metabase · Notion Product Analytics – Amplitude Certification (2022)
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, 11:02:20 AMsystemanalysis completed{"PM_score":72,"SPM_score":43}
- 9/29/2026, 11:01:27 AMsystemcv ingested{"file":"pm_10_rohan_sane.pdf","role":"PM","redactions":{"name":1,"email":1,"phone":1,"profile_url":1},"name_detected":true}