Dashboard / Candidate

Pranav Joshi

Placed in Senior Product Manager by best fit (no role stated; scored higher on this rubric) · 17_pranav_joshi.pdf

Private: squad_4@pg27.mesaschool.co · no phone found

Applied-role score

42/100

#10 of 16 SPM applicants

PM score

42/100

SPM score

42/100

Current status

Analysed

Below shortlist (AI suggestion)

Your decision

Why they fit

Problem -> Change -> Business/Operational Result 20%
The candidate provides multiple strong, quantified examples connecting their direct actions to significant operational and business results, including reducing false positives by 35%, cutting pre-exam resolution time by 35%, and decreasing customer handling time by 45%.

Where they fall short

Independent Decision Ownership −24 pts
The CV mentions enabling data-backed product decisions and defining feature scope, but provides only indirect participation evidence without any specific consequential decisions made under uncertainty or trade-offs owned by the candidate. Evidence of independent decision ownership is not present.
Evidence-Led Prioritisation and Reversal −16 pts
While the CV lists backlog prioritization and data-backed decisions as competencies, there are no specific examples showing evidence used to decide what to stop or alter, and no example of a directional reversal based on new evidence. Evidence of evidence-led prioritisation and reversal is not present.
Creates Systems Others Run −9 pts
The candidate created operational workflows and authored PRDs and release documentation to support rollouts and reduce reactive support dependencies. However, there is no evidence of establishing an enduring framework, operating rhythm, or system that was adopted and run independently by other teams as standard practice.

Rubric breakdown: PM

SPM (applied)PM

Criterion	Weight	Score	Weighted	AI reasoning	Evidence from CV
Problem -> Change -> Result	25%	4/5	20.0/25	The candidate provides clear examples of identifying workflow frictions and implementing product or process changes that produced measurable improvements, such as cutting handling time by 45% and pre-exam resolution time by 35%. While individual bullets present strong, quantified outcomes tied to candidate action, detailed context regarding the initial problem discovery is concise.	“Optimized pan-India center-based exam workflows, reducing pre-exam issue resolution time by 35% and minimizing live-exam escalations across all centers.” “Improved agent journeys by automating data access and repetitive workflows, reducing average customer handling time per account by 45%.”
Evidence-Led Decisions	20%	1/5	4.0/20	The CV mentions leveraging SQL and data validation to diagnose root causes and enable data-backed decisions, but it never details a specific decision that was changed, stopped, or prioritized based on that data. There is no explanation of the reasoning or what happened after any specific decision was made.	“Leveraged SQL-driven analysis and structured data validation to diagnose platform issues, uncover root causes, and enable data-backed product decisions.”
Builds Repeatable Ways of Working	20%	1/5	4.0/20	The candidate created standard product artifacts such as PRDs and release documentation, but there is no evidence that they created a reusable operating method, template, or system that was adopted by others as an ongoing practice.	“Authored PRDs and release documentation to support smooth enterprise rollouts and reduce dependency on reactive support.”
Carries Ambiguous Problems Through to Closure	20%	2/5	8.0/20	The CV shows evidence of cross-functional alignment and resolving multi-center exam issues to minimize escalations. However, it lacks a detailed example of owning a messy, ambiguous problem from initial identification through post-fix follow-up.	“Optimized pan-India center-based exam workflows, reducing pre-exam issue resolution time by 35% and minimizing live-exam escalations across all centers.” “Partnered with engineering, QA, and client teams to ship features across enterprise implementations, reducing post-release escalations.”
Gets to the Source Before Designing the Fix	15%	2/5	6.0/15	The candidate led cross-functional discovery across support and operations teams, which helped reduce requirement changes. However, there is no direct evidence of firsthand user observation, shadowing, or interviews showing that the design pivoted due to discoveries made at the source.	“Led cross-functional requirement discovery across sales, support, and operations teams, reducing post-build requirement changes by 15%.”
Total	100%		42	The candidate shows strong capability in delivering workflow and product changes tied to concrete, measurable efficiency metrics. However, the CV provides limited evidence of evidence-led decision pivots, firsthand problem discovery with end users, or building repeatable operational practices adopted by others.

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.

Evidence

Verified CV quotes used (SPM rubric)

“Revamped an AI-enabled proctoring platform (SaaS + PaaS), improving cheating detection accuracy, reducing false positives by 35%, and supporting 5L+ concurrent exams without downtime.” · Problem -> Change -> Business/Operational Result
“Optimized pan-India center-based exam workflows, reducing pre-exam issue resolution time by 35% and minimizing live-exam escalations across all centers.” · Problem -> Change -> Business/Operational Result
“Defined workflows and feature scope for an AI-powered agent platform, accelerating enterprise adoption and reducing new-agent onboarding time by 30%.” · Problem -> Change -> Business/Operational Result
“Improved agent journeys by automating data access and repetitive workflows, reducing average customer handling time per account by 45%.” · Problem -> Change -> Business/Operational Result
“Optimized pan-India center-based exam workflows, reducing pre-exam issue resolution time by 35% and minimizing live-exam escalations across all centers.” · Creates Systems Others Run
“Authored PRDs and release documentation to support smooth enterprise rollouts and reduce dependency on reactive support.” · Creates Systems Others Run
“Optimized pan-India center-based exam workflows, reducing pre-exam issue resolution time by 35% and minimizing live-exam escalations across all centers.” · Multi-Party Resolution Under Pressure
“Partnered with engineering, QA, and client teams to ship features across enterprise implementations, reducing post-release escalations.” · Multi-Party Resolution Under Pressure

Outcomes stated in the CV

Reduced false positives by 35% and supported 5L+ concurrent exams without downtime on an AI-enabled proctoring platform.
Delivered the first version of an interview platform within 2.5 months with 100% milestone adherence.
Reduced pre-exam issue resolution time by 35% across pan-India center-based exams.
Reduced post-build requirement changes by 15% and accelerated stakeholder alignment by 25%.
Reduced average customer handling time per account by 45% and new-agent onboarding time by 30%.

Anonymised CV (exactly what the AI saw)

Email:
Portfolio: Mobile:
LinkedIn: [PROFILE URL REDACTED] /
Summary
Associate Product Manager with 3.5+ years of experience building and scaling SaaS platforms across FinTech
and EdTech. Proven track record in 0-1 launches, AI-enabled product optimization, and data-driven platform
improvements delivering up to 45% operational efficiency gains. Strong in roadmap execution, KPI ownership, and
cross-functional leadership.
Work Experience
• Think Exam (GingerWebs) Noida, India
Associate Product Manager July 2025 - Present
◦ Product Revamp & Quality Improvement: Revamped an AI-enabled proctoring platform (SaaS + PaaS),
improving cheating detection accuracy, reducing false positives by 35%, and supporting 5L+ concurrent exams without
downtime.
◦ 0→1 SaaS Product Launch: Built and launched an interview platform enabling AI-based and manual interviews,
delivering the first version within 2.5 months with 100% milestone adherence.
◦ Pre-Exam Issue Resolution (Pan India): Optimized pan-India center-based exam workflows, reducing pre-exam
issue resolution time by 35% and minimizing live-exam escalations across all centers.
◦ Stakeholder Management: Led cross-functional requirement discovery across sales, support, and operations teams,
reducing post-build requirement changes by 15%.
◦ Product Vision & Prototyping: Created end-to-end workflows and AI-assisted prototypes to communicate product
vision, accelerating stakeholder alignment by 25%.
• C&R Software Bangalore, India
Consultant July 2022 - July 2025
◦ B2B GTM & Product Adoption: Defined workflows and feature scope for an AI-powered agent platform,
accelerating enterprise adoption and reducing new-agent onboarding time by 30%.
◦ User Experience & Productivity Optimization: Improved agent journeys by automating data access and repetitive
workflows, reducing average customer handling time per account by 45%.
◦ Data Integrity & Platform Accuracy: Partnered with US banking stakeholders to translate collections workflows
into scalable product enhancements, improving recovery efficiency and platform reliability
◦ Analytical Problem Solving : : Leveraged SQL-driven analysis and structured data validation to diagnose platform
issues, uncover root causes, and enable data-backed product decisions.
◦ Cross-Functional Stakeholder Management: Partnered with engineering, QA, and client teams to ship features
across enterprise implementations, reducing post-release escalations.
◦ Product Artifacts: Authored PRDs and release documentation to support smooth enterprise rollouts and reduce
dependency on reactive support.
• IQVIA Delhi, India
Project Associate Feb 2022 - Jul 2022
◦ Team Leadership: Led a 10-member intern team for a nationwide MOHUA(Ministry of Housing and Urban Affairs)
project, coordinating deliverables and reporting to senior stakeholders.
◦ Data Analysis: Analysed 50K+ survey responses to improve data quality and insights, identifying key trends and
anomalies for stakeholder reporting.
◦ Reporting: Built BI dashboards and reports for internal and government stakeholders, enabling data-driven decision
making and transparency.
Skills
• Core Competencies: Roadmapping, PRDs, User Stories, KPI Definition, GTM, A/B Testing, Backlog Prioritization,
Product Strategy, Agile Methodologies, Market Research, Product Analytics, Stakeholder Management
• Analytics & Tech : SQL, Python, Data Validation, API Understanding, AWS
• Tools: Jira, Google Analytics, Figma, Tableau, SSRS, Pentaho, MS Office (Word, Excel, PPT)
Education
• B.Tech, Computer Science Jaipur, India
Jaipur National University 2018 - 2022
[CANDIDATE] JoshiPRANAV [CANDIDATE] [EMAIL REDACTED]
+91 99014 3718299014 37182
[CANDIDATE]-[CANDIDATE]-pm
https://pranavjoshi.vercel.app/

Redacted: name ×4, email ×1, profile url ×1

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Email draft

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Audit trail (3)

9/29/2026, 11:36:32 AMsystemanalysis completed{"PM_score":42,"SPM_score":42}
9/29/2026, 11:36:32 AMsystemrole assigned best fit{"role":"SPM","PM_score":42,"SPM_score":42}
9/29/2026, 11:29:31 AMsystemcv ingested{"file":"17_pranav_joshi.pdf","role":"AUTO","redactions":{"name":4,"email":1,"profile_url":1},"name_detected":true}