Dashboard / Candidate
Arjun Verma
Placed in Product Manager by best fit (no role stated; scored higher on this rubric) · 04_arjun_verma.pdf
Private: squad_1@pg27.mesaschool.co · no phone found
Applied-role score
56/100#7 of 34 PM applicants
PM score
56/100SPM score
45/100Current status
AnalysedBorderline (AI suggestion)
Your decision
1
Why they fit
- Problem -> Change -> Result 25%
The CV presents multiple strong, concrete examples of the candidate identifying operational friction or workflow inefficiencies and delivering technical or UX changes that produced measurable improvements in adoption, overhead, and support volume.
- Builds Repeatable Ways of Working 20%
The candidate has built repeatable operational methods and tools, including an internal automation tool that business team members adopted to save weekly reporting time, and standardized assessment scoring logic used across hiring rounds.
2
Where they fall short
- Carries Ambiguous Problems Through to Closure −16 pts
The CV mentions serving as a cross-functional conduit between clients, end users, and engineering, but provides no specific example of navigating an ambiguous escalation or failure through to completion and follow-up.
- Evidence-Led Decisions −12 pts
The candidate shows that feedback informed product fixes and dashboard iterations, but the CV does not show explicit trade-offs, rationale, or decisions being altered, deprioritised, or stopped based on evidence.
- Gets to the Source Before Designing the Fix −12 pts
While the candidate analyzed aggregated feedback loops and user feedback, there is no evidence of directly observing, interviewing, or working alongside users to conduct firsthand discovery before shaping a solution.
| Criterion | Weight | Score | Weighted | AI reasoning | Evidence from CV |
|---|---|---|---|---|---|
| Independent Decision Ownership | 30% | 1/5 | 6.0/30 | While the candidate lists delivery and execution of multiple features and integrations, explicit evidence of making autonomous, consequential decisions with trade-offs under uncertainty is not present. The profile reflects product delivery and execution rather than independent decision ownership. | Evidence not present in the CV. |
| Problem -> Change -> Business/Operational Result | 20% | 5/5 | 20.0/20 | The candidate provides repeated, strong examples connecting their direct actions to quantified operational and business outcomes across adoption, cycle time reduction, and support volume reduction. These interventions clearly articulate the problem, the specific change made, and measurable results. |
1 AI quote(s) discarded: not found verbatim in CV |
| Evidence-Led Prioritisation and Reversal | 20% | 1/5 | 4.0/20 | The CV shows indirect evidence of using user feedback to iterate on dashboards and fix friction points, but explicit evidence of evidence-led prioritization, stopping work, or reversing an earlier direction is not present. | Evidence not present in the CV. |
| Creates Systems Others Run | 15% | 4/5 | 12.0/15 | The candidate built multiple repeatable systems, frameworks, and automations that were adopted by other teams to improve operations and testing efficiency. These systems produced tangible, ongoing operational benefits for internal stakeholders. |
|
| Multi-Party Resolution Under Pressure | 15% | 1/5 | 3.0/15 | The CV mentions acting as a conduit between corporate clients, end users, and engineering, which provides indirect evidence of multi-stakeholder coordination. However, explicit evidence of resolving critical incidents, managing escalations, or steering through uncertainty under pressure is not present. | Evidence not present in the CV. |
| Total | 100% | 45 | The candidate demonstrates strong capabilities in translating operational problems into measurable product outcomes and creating systems that other teams adopt and run. However, the CV lacks evidence of independent decision ownership under uncertainty, evidence-led direction reversals, and high-pressure multi-party escalation resolution, making them a partial fit for the Senior Product Manager rubric. | ||
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)
- “Analyzed cross-platform feedback loops to identify systemic friction points, leading to product fixes that reduced candidate support tickets by 50% (From 500+ to ~200/campaign).” · Problem -> Change -> Result
- “Engineered internal analysis automation using tools like Google Apps Script, MetaBase, Excel, etc. to aggregate platform performance metrics, saving the business team 15+ hours/week in manual reporting.” · Problem -> Change -> Result
- “Directed API-led integrations with SAP SuccessFactors, Workday and external vendors, automating candidate data synchronization and reducing manual recruiter overhead by 40%.” · Problem -> Change -> Result
- “Analyzed cross-platform feedback loops to identify systemic friction points, leading to product fixes that reduced candidate support tickets by 50% (From 500+ to ~200/campaign).” · Evidence-Led Decisions
- “Scaled the Campus Ambassador Program to 130+ leads; utilized user feedback to iterate on internal ambassador dashboards, driving a 25% increase in engagement.” · Evidence-Led Decisions
- “Engineered internal analysis automation using tools like Google Apps Script, MetaBase, Excel, etc. to aggregate platform performance metrics, saving the business team 15+ hours/week in manual reporting.” · Builds Repeatable Ways of Working
- “Standardized scoring logic across heterogeneous assessment types (Writing, Coding, Psychometric), ensuring "Apples-to-Apples" candidate comparison in high-stakes hiring rounds.” · Builds Repeatable Ways of Working
- “Acted as the primary conduit between 20+ Tier-1 corporate clients, the end users and the Engineering team.” · Carries Ambiguous Problems Through to Closure
- “Analyzed cross-platform feedback loops to identify systemic friction points, leading to product fixes that reduced candidate support tickets by 50% (From 500+ to ~200/campaign).” · Gets to the Source Before Designing the Fix
Outcomes stated in the CV
- Automated screening for 1.5L+ candidates and reduced time-to-screen by 70% for enterprise clients via Unified AI Assessment Engine.
- Onboarded 200+ colleges and facilitated 2.6M+ student registrations via the TPO Tracking Dashboard.
- Drove a 20% growth in recruiter adoption and a 30% reduction in time-per-action through redesign of the Recruiter Journey & Panels.
- Reduced candidate support tickets by 50% (from 500+ to ~200/campaign) through systemic friction fixes.
- Cut platform testing time by 30% by spearheading an Activity Logs Architecture.
- Saved business team 15+ hours/week in manual reporting by engineering automated performance metric reporting.
Anonymised CV (exactly what the AI saw)
New Delhi, Delhi, India SUMMARY Data-driven Associate Product Manager with 2+ years of experience in the B2B SaaS and Assessment Tech domain, specializing in scaling candidate lifecycle management systems. Proven track record of scaling platforms to 2.6M+ users and driving a 20% increase in recruiter adoption through data-driven redesigns and API integrations. Proficient in translating complex enterprise requirements into scalable system architectures. Technical edge in SQL, API-first design, and Agentic AI workflows. EXPERIENCE Associate Product Manager Unstop March 2025 - Present New Delhi, Delhi, IN, • Unified AI Assessment Engine: Orchestrated a high-scale evaluation ecosystem integrating AI Screening, CEFR-calibrated Speech Analytics, and Secure Proctoring; automated screening for 1.5L+ candidates and achieved a 70% reduction in time-to-screen for enterprise clients. • Product-Led Growth (PLG): Launched the TPO Tracking Dashboard, enabling institutional scale by onboarding 200+ colleges and facilitating 2.6M+ student registrations. • Product Adoption & UX Architecture: Led the end-to-end redesign of the Recruiter Journey & Panels; optimized navigation and candidate- listing visibility, resulting in a 20% growth in recruiter adoption and a 30% reduction in time-per-action. • Enterprise Roadmap Delivery: Managed the delivery of 100+ custom features for marquee clients like Flipkart, Adobe, Indigo, etc., including complex customizable workflows and multi-round automated pipelines. • System Interoperability: Directed API-led integrations with SAP SuccessFactors, Workday and external vendors, automating candidate data synchronization and reducing manual recruiter overhead by 40%. Associate, Quality Assurance Engineer Unstop March 2024 - March 2025 New Delhi, Delhi, IN, • User Experience Optimization: Analyzed cross-platform feedback loops to identify systemic friction points, leading to product fixes that reduced candidate support tickets by 50% (From 500+ to ~200/campaign). • Unified Audit Architecture: Spearheaded the mapping of an "Activity Logs Architecture", mapping every candidate or recruiter state transition to a single audit trail, cutting platform testing time by 30%. • Evaluation Normalization: Standardized scoring logic across heterogeneous assessment types (Writing, Coding, Psychometric), ensuring "Apples-to-Apples" candidate comparison in high-stakes hiring rounds. Associate, Business and Website Operations Unstop July 2023 - March 2024 New Delhi, Delhi, IN, • Process Automation: Engineered internal analysis automation using tools like Google Apps Script, MetaBase, Excel, etc. to aggregate platform performance metrics, saving the business team 15+ hours/week in manual reporting. • Community Growth: Scaled the Campus Ambassador Program to 130+ leads; utilized user feedback to iterate on internal ambassador dashboards, driving a 25% increase in engagement. • Cross-Stakeholder Collaboration: Acted as the primary conduit between 20+ Tier-1 corporate clients, the end users and the Engineering team. SKILLS Product Strategy: Product Roadmapping, Product-Led Growth (PLG), Backlog Prioritization, Competitor Benchmarking, PRD/User Story Authoring Product Discovery: User Research, A/B Testing, MS Clarity, Root Cause Analysis, Wireframing (Figma) Languages and Tools: SQL, Python, MS Excel, Google Analytics, MoEngage, Power BI, MetaBase, Figma, Miro, Mermaid.js, Jira, ClickUp, MATLAB Collaboration and Delivery: Agile, Scrum, Kanban, Cross-functional Leadership, Stakeholder Management Technical: API Integration (REST), Microservices, Generative & Agentic AI, Selenium, AWS EDUCATION Bachelors of Sciences (Hons.) in Electronics and Communications Minor in Mathematics • Sri Venkateswara College, University of Delhi • New Delhi, Delhi, IN • 2023 INVOLVEMENT President, NSS unit of Sri Venkateswara College New Delhi, Delhi, IN • National Service Scheme • September 2021 - July 2023 • Led a cross-functional team of 120+ volunteers across 6 specialized wings to execute 40+ large-scale events, successfully serving a community of 5,000+ participants. • Directed 5 flagship initiatives in education and healthcare for marginalized communities, overseeing end-to-end logistics, resource budgeting, and volunteer training modules. [CANDIDATE] VERMAArjun [CANDIDATE] [EMAIL REDACTED] +91 98674 2019398674 20193 [CANDIDATE]-[CANDIDATE]-pmin/[CANDIDATE]-[CANDIDATE]-pm
Redacted: name ×6, email ×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 (3)
- 9/29/2026, 11:35:22 AMsystemanalysis completed{"PM_score":56,"SPM_score":45}
- 9/29/2026, 11:35:22 AMsystemrole assigned best fit{"role":"PM","PM_score":56,"SPM_score":45}
- 9/29/2026, 11:29:27 AMsystemcv ingested{"file":"04_arjun_verma.pdf","role":"AUTO","redactions":{"name":19,"email":1},"name_detected":true}