Dashboard / Candidate
Suresh Raj
Placed in Product Manager by best fit (no role stated; scored higher on this rubric) · 12_suresh_raj.pdf
Private: squad_3@pg27.mesaschool.co · no phone found
Applied-role score
47/100#14 of 34 PM applicants
PM score
47/100SPM score
39/100Current status
AnalysedBelow shortlist (AI suggestion)
Your decision
1
Why they fit
- Problem -> Change -> Result 25%
The candidate provides clear examples of identifying friction or operational pain points, implementing targeted solutions, and measuring the resulting outcomes. Specifically, chatbot iterations based on live conversation drop-offs and automation workflows both demonstrate candidate-led actions directly linked to concrete improvements.
- Evidence-Led Decisions 20%
The CV shows a clear example of analyzing live operational metrics (CSAT, resolution rate, and drop-offs) to adjust conversation flows and prompt designs, followed by measurable increases in satisfaction and decreases in unresolved queries.
2
Where they fall short
- Carries Ambiguous Problems Through to Closure −20 pts
Evidence is not present. The CV focuses on shipping features, growth experiments, and automated workflows, with no examples of driving an ambiguous or messy problem through stakeholder escalations to closure with post-resolution follow-up.
- Builds Repeatable Ways of Working −12 pts
The candidate implemented a UTM tracking framework across multiple acquisition channels and worked on sales CRM workflows, showing partial elements of establishing operational structure. However, there is no explicit evidence that these practices or tools were adopted, maintained, or reused by other team members as an ongoing operating method.
- Gets to the Source Before Designing the Fix −12 pts
The CV includes only indirect or participation-level evidence of using sales feedback during an internship to refine lead stages. There is no evidence of directly observing, interviewing, or shadowing users to uncover root causes before designing a fix.
| 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 friction or operational pain points, implementing targeted solutions, and measuring the resulting outcomes. Specifically, chatbot iterations based on live conversation drop-offs and automation workflows both demonstrate candidate-led actions directly linked to concrete improvements. |
|
| Evidence-Led Decisions | 20% | 4/5 | 16.0/20 | The CV shows a clear example of analyzing live operational metrics (CSAT, resolution rate, and drop-offs) to adjust conversation flows and prompt designs, followed by measurable increases in satisfaction and decreases in unresolved queries. |
|
| Builds Repeatable Ways of Working | 20% | 2/5 | 8.0/20 | The candidate implemented a UTM tracking framework across multiple acquisition channels and worked on sales CRM workflows, showing partial elements of establishing operational structure. However, there is no explicit evidence that these practices or tools were adopted, maintained, or reused by other team members as an ongoing operating method. |
|
| Carries Ambiguous Problems Through to Closure | 20% | 0/5 | 0.0/20 | Evidence is not present. The CV focuses on shipping features, growth experiments, and automated workflows, with no examples of driving an ambiguous or messy problem through stakeholder escalations to closure with post-resolution follow-up. | Evidence not present in the CV. |
| Gets to the Source Before Designing the Fix | 15% | 1/5 | 3.0/15 | The CV includes only indirect or participation-level evidence of using sales feedback during an internship to refine lead stages. There is no evidence of directly observing, interviewing, or shadowing users to uncover root causes before designing a fix. |
|
| Total | 100% | 47 | The candidate demonstrates solid capability in executing product initiatives, automating manual workflows, and using live quantitative data to iterate on features such as chatbots and onboarding funnels. However, the CV lacks evidence for carrying ambiguous, multi-stakeholder problems through to resolution, establishing repeatable ways of working adopted by others, and conducting firsthand qualitative user discovery. | ||
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)
- “Built and iterated on a production chatbot by designing conversation flows and fallback logic; analyzed CSAT, resolution rate, and drop-offs from live conversations to drive prompt and flow changes, improving CSAT by ~15% and reducing unresolved queries by ~20%.” · Problem -> Change -> Result
- “Implemented automation workflows and AI agents (n8n, no-code tools) to streamline processes, reducing manual operational effort by ~30%” · Problem -> Change -> Result
- “Developed a fraud prevention mechanism that detected and blocked ~18% of fraud signups, ensuring fair user growth.” · Problem -> Change -> Result
- “Built and iterated on a production chatbot by designing conversation flows and fallback logic; analyzed CSAT, resolution rate, and drop-offs from live conversations to drive prompt and flow changes, improving CSAT by ~15% and reducing unresolved queries by ~20%.” · Evidence-Led Decisions
- “Implemented a UTM tracking framework across 10+ acquisition channels to enable campaign-level attribution.” · Builds Repeatable Ways of Working
- “Worked on CRM workflows used by sales teams to manage leads, accounts, and client follow-ups.” · Builds Repeatable Ways of Working
- “Helped define and improve lead lifecycle stages (lead → qualified → converted) based on sales feedback.” · Gets to the Source Before Designing the Fix
Outcomes stated in the CV
- Achieved 35% referral-to-signup conversion rate driving ~120K new users in 4 months.
- Achieved 55% FTUE completion rate, leading to 80K+ users completing onboarding in the first week.
- Enabled 1.5M+ NFTs minted as user identity artifacts with 70% customized within 7 days.
- Detected and blocked ~18% of fraud signups with a fraud prevention mechanism.
- Improved CSAT by ~15% and reduced unresolved queries by ~20% through production chatbot iterations.
- Achieved 45%+ CTR and ~25% uplift in feature adoption and campaign completion from custom in-app overlays.
- Drove 1 billion units of in-platform currency redeemed in 2 months and boosted daily active engagement by 18% via rKade Spin & Slot games.
- Reduced manual operational effort by ~30% using n8n and no-code automation workflows and AI agents.
- Shipped a no-code e-commerce website in 3 days generating ₹10L+ in merchandise sales.
Anonymised CV (exactly what the AI saw)
| |[PROFILE URL REDACTED] Product Manager with experience across AI, gaming, digital products, and marketplaces. Strong in roadmap ownership, cross-functional execution, and feature launches that drive engagement and business impact. Experienced in AI-driven automation, GTM, growth, market research, and data-driven decision-making across B2B, B2C, and SaaS products. EXPERIENCE KGeN & Humyn Labs | Associate Product manager Bangalore | Feb 2025 - Present ● Drove product strategy for Kgen’s platform (app + website), collaborating with engineering, design, and growth teams. ● Built milestone-based referral system achieving 35% referral→signup conversion rate driving ~120K new users in 4 months. ● Crafted FTUE flows that achieved a 55% completion rate, leading to 80K+ users completing onboarding within the first week. ● Designed onboarding journeys with Web3 integration, enabling 1.5M + NFTs minted as user identity artifacts; 70% of users customized their profile NFTs within 7 days. ● Implemented a UTM tracking framework across 10+ acquisition channels to enable campaign-level attribution. ● Developed a fraud prevention mechanism that detected and blocked ~18% of fraud signups, ensuring fair user growth. ● Built and iterated on a production chatbot by designing conversation flows and fallback logic; analyzed CSAT, resolution rate, and drop-offs from live conversations to drive prompt and flow changes, improving CSAT by ~15% and reducing unresolved queries by ~20%. ● Launched custom in-app overlays to announce new features, achieving 45%+ click-through rates and driving ~25% uplift in feature adoption and campaign completion within the first week of release. ● Introduced rKade Spin & Slot games for redeeming on-platform currency, resulting in 1 billion units of in-platform currency redeemed within 2 months and boosting daily active engagement by 18%. ● Implemented automation workflows and AI agents (n8n, no-code tools) to streamline processes, reducing manual operational effort by ~30% ● Shipped a no-code e-commerce website using Lovable and Shopify in 3 days, generating ₹10L+ in merchandise sales. Growtholics | Product Intern Bangalore | Jul 2024 - Jan 2025 ● Worked on CRM workflows used by sales teams to manage leads, accounts, and client follow-ups. ● Helped define and improve lead lifecycle stages (lead → qualified → converted) based on sales feedback. ● Analyzed CRM data to identify drop-offs and inefficiencies in the sales funnel. PROJECTS ● Tinder + | Case study to increase post match conversion ● Launchpad | AI-Powered Job Application Platform ● Ola RescueNet | An in-app concept for stray animal reporting, rescue coordination, and micro-donations. SKILLS ● Product Management: Roadmap Planning, PRDs, Agile/Scrum, A/B Testing, Stakeholder Management ● PM & Collaboration Tools: Miro, Figma, JIRA, Confluence, Trello, Notion, Lovable, Mixpanel ● Automation & AI: n8n, Workflow Automations, AI Agents, GenAI, LangChain, Huggingface, MCP, RAG ● Analytics & Tools: SQL, Python, Excel, Power BI, Tableau ● Tech & ML:APIs, Frontend, Databases, AWS Cloud ● Other: Market Research, User Research, EDA CERTIFICATIONS Machine Learning A-Z: AI, Python & R | DataScience(Cisco) | Alteryx Foundation (Alteryx ) | Artificial Intelligence Micro Certification(Product School) | Product Management(Product Space) EDUCATION ● BACHELOR OF TECHNOLOGY – CMR University Majors: Computer Science and Engineering, ● Product Management and Gen AI - IIT Kanpur Executive Program [CANDIDATE] RajSURESH [CANDIDATE] [EMAIL REDACTED] +91 99003 1784299003 17842
Redacted: name ×2, email ×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 (3)
- 9/29/2026, 11:35:43 AMsystemanalysis completed{"PM_score":47,"SPM_score":39}
- 9/29/2026, 11:35:43 AMsystemrole assigned best fit{"role":"PM","PM_score":47,"SPM_score":39}
- 9/29/2026, 11:29:29 AMsystemcv ingested{"file":"12_suresh_raj.pdf","role":"AUTO","redactions":{"name":2,"email":1,"profile_url":1},"name_detected":true}