Kargo HiringEmail sending LIVE
Dashboard / Candidate

Rohan Basu

Placed in Product Manager by best fit (no role stated; scored higher on this rubric) · 29_rohan_basu.pdf
Private: squad_6@pg27.mesaschool.co · no phone found
Applied-role score
55/100
#9 of 34 PM applicants
PM score
55/100
SPM score
49/100
Current status
Analysed
Borderline (AI suggestion)
Your decision
1

Why they fit

  • Problem -> Change -> Result 25%

    The CV provides repeated, strong examples of identifying friction points in compliance and onboarding, implementing product solutions, and delivering substantial measurable outcomes such as an 80% ticket reduction and an 18% conversion lift.

2

Where they fall short

  • Evidence-Led Decisions −12 pts

    The candidate notes making principled architectural decisions against high-maintenance designs based on evaluated constraints, and conducting user research and drop-off analysis. However, the CV lacks detailed narrative showing a specific decision being changed, stopped, or reprioritised in response to evidence along with what happened afterward.

  • Builds Repeatable Ways of Working −12 pts

    The CV highlights instrumenting tracking events that reduced rework across teams and establishing foundational processes from scratch. It partially meets the criteria but lacks explicit evidence of designing a reusable tool, template, or operating framework that was subsequently adopted by others as standard practice.

  • Carries Ambiguous Problems Through to Closure −12 pts

    The candidate demonstrates cross-functional execution across engineering, compliance, and external vendors in complex regulatory domains. However, there is no direct evidence of resolving a messy failure or operational escalation and driving long-term post-fix closure.

3

Rubric breakdown: PM

PM (applied)SPM
CriterionWeightScoreWeightedAI reasoningEvidence from CV
Problem -> Change -> Result25% 5/525.0/25The CV provides repeated, strong examples of identifying friction points in compliance and onboarding, implementing product solutions, and delivering substantial measurable outcomes such as an 80% ticket reduction and an 18% conversion lift.
  • “Improved transaction conversion by 18% by simplifying high-friction compliance steps and error-recovery flows, unlocking approximately $1.6M in incremental monthly transaction value.”
  • “Launched multilingual, AI-powered support and intent-resolution flows with human-in-the-loop safeguards, reducing inbound support tickets by ~80% while preserving accuracy, and customer trust.”
  • “Led redesign of first-time user KYC onboarding in partnership with third-party verification vendors and compliance teams; introduced clearer status transparency and recovery flows, increasing successful verifications by 36% while maintaining regulatory adherence.”
Evidence-Led Decisions20% 2/58.0/20The candidate notes making principled architectural decisions against high-maintenance designs based on evaluated constraints, and conducting user research and drop-off analysis. However, the CV lacks detailed narrative showing a specific decision being changed, stopped, or reprioritised in response to evidence along with what happened afterward.
  • “Identified scalability constraints in UI-dependent automation systems and made principled decisions against high-maintenance architectures.”
  • “Led user research and structured experimentation driving a 109% increase in one-time investments.”
Builds Repeatable Ways of Working20% 2/58.0/20The CV highlights instrumenting tracking events that reduced rework across teams and establishing foundational processes from scratch. It partially meets the criteria but lacks explicit evidence of designing a reusable tool, template, or operating framework that was subsequently adopted by others as standard practice.
  • “Instrumented key payment and investment events, reducing development rework by ~20% and improving cross-team clarity.”
  • “Established core product foundations from zero, defining product vision, roadmap, success metrics, and backlog prioritization to support rapid scale.”
Carries Ambiguous Problems Through to Closure20% 2/58.0/20The candidate demonstrates cross-functional execution across engineering, compliance, and external vendors in complex regulatory domains. However, there is no direct evidence of resolving a messy failure or operational escalation and driving long-term post-fix closure.
  • “Led redesign of first-time user KYC onboarding in partnership with third-party verification vendors and compliance teams; introduced clearer status transparency and recovery flows, increasing successful verifications by 36% while maintaining regulatory adherence.”
  • “Partnered with regional, engineering, and compliance teams to launch digital payment experiences across Peru, Panama, and Argentina, driving 24–28% transaction growth across markets.”
Gets to the Source Before Designing the Fix15% 2/56.0/15The CV mentions leading user research and running stakeholder workshops, but it does not describe firsthand immersion with affected users or show specifically how discoveries directly altered product design choices.
  • “Led user research and structured experimentation driving a 109% increase in one-time investments.”
  • “Led global stakeholder workshops and delivered Agile user stories with SQL-backed insights.”
Total100%55The candidate demonstrates exceptional strength in executing problem-to-result cycles, consistently delivering large-scale, quantifiable business impact across conversion and support reduction. However, the profile provides limited depth on firsthand qualitative user discovery, establishing reusable team processes, and navigating complex operational escalations to full 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)
  • “Improved transaction conversion by 18% by simplifying high-friction compliance steps and error-recovery flows, unlocking approximately $1.6M in incremental monthly transaction value.” · Problem -> Change -> Result
  • “Launched multilingual, AI-powered support and intent-resolution flows with human-in-the-loop safeguards, reducing inbound support tickets by ~80% while preserving accuracy, and customer trust.” · Problem -> Change -> Result
  • “Led redesign of first-time user KYC onboarding in partnership with third-party verification vendors and compliance teams; introduced clearer status transparency and recovery flows, increasing successful verifications by 36% while maintaining regulatory adherence.” · Problem -> Change -> Result
  • “Identified scalability constraints in UI-dependent automation systems and made principled decisions against high-maintenance architectures.” · Evidence-Led Decisions
  • “Led user research and structured experimentation driving a 109% increase in one-time investments.” · Evidence-Led Decisions
  • “Instrumented key payment and investment events, reducing development rework by ~20% and improving cross-team clarity.” · Builds Repeatable Ways of Working
  • “Established core product foundations from zero, defining product vision, roadmap, success metrics, and backlog prioritization to support rapid scale.” · Builds Repeatable Ways of Working
  • “Led redesign of first-time user KYC onboarding in partnership with third-party verification vendors and compliance teams; introduced clearer status transparency and recovery flows, increasing successful verifications by 36% while maintaining regulatory adherence.” · Carries Ambiguous Problems Through to Closure
  • “Partnered with regional, engineering, and compliance teams to launch digital payment experiences across Peru, Panama, and Argentina, driving 24–28% transaction growth across markets.” · Carries Ambiguous Problems Through to Closure
  • “Led user research and structured experimentation driving a 109% increase in one-time investments.” · Gets to the Source Before Designing the Fix
  • “Led global stakeholder workshops and delivered Agile user stories with SQL-backed insights.” · Gets to the Source Before Designing the Fix
Outcomes stated in the CV
  • Increased successful KYC verifications by 36% through redesigned onboarding and recovery flows at Western Union.
  • Unlocked approximately $1.6M in incremental monthly transaction value via an 18% improvement in transaction conversion at Western Union.
  • Reduced inbound support tickets by ~80% by launching multilingual AI-powered support flows with human-in-the-loop safeguards.
  • Drove 24–28% transaction growth across Peru, Panama, and Argentina through digital payment rollouts.
  • Achieved a 25% increase in recurring automated transactions for mandate-based investment products at Jar App.
  • Drove a 109% increase in one-time investments through structured experimentation and user research at Jar App.
  • Reduced invoice disputes by 30% via billing automation initiatives at Accenture.
Anonymised CV (exactly what the AI saw)
[PROFILE URL REDACTED]
SUMMARY
Product Manager with 8+ years building and scaling high-impact consumer fintech products across payments,
compliance, and automated investment systems. Proven track record driving measurable conversion
improvements, reducing friction in regulated money-movement workflows, and leading 0→1 product launches in
ambiguity-heavy environments. Strong cross-functional leader partnering with Engineering, Risk, and
Operations to deliver scalable, data-driven outcomes. Experience designing and shipping LLM-powered
systems with structured evaluation and reliability frameworks.
CORE SKILLS
● Product: 0→1 Launches • Consumer Growth • Payments & Compliance • Roadmapping •
Experimentation • Metrics & KPIs
● Data: Funnel Optimization • Behavioral Drop-off Analysis • Conversion Improvement • Cost Efficiency
● AI Systems: LLM Product Design • Evaluation Frameworks • Reliability & Confidence Modeling
EXPERIENCE
Product Manager
Western Union – Pune
February 2024 – August 2025
● Owned end-to-end sender and receiver journeys for regulated cross-border payment flows across
multiple LACA markets, balancing compliance, conversion, reliability, and transaction integrity at scale.
● Led redesign of first-time user KYC onboarding in partnership with third-party verification vendors and
compliance teams; introduced clearer status transparency and recovery flows, increasing successful
verifications by 36% while maintaining regulatory adherence.
● Improved transaction conversion by 18% by simplifying high-friction compliance steps and
error-recovery flows, unlocking approximately $1.6M in incremental monthly transaction value.
● Launched multilingual, AI-powered support and intent-resolution flows with human-in-the-loop
safeguards, reducing inbound support tickets by ~80% while preserving accuracy, and customer trust.
● Partnered with regional, engineering, and compliance teams to launch digital payment experiences
across Peru, Panama, and Argentina, driving 24–28% transaction growth across markets.
● Owned product roadmap and success metrics for regulated payment journeys; managed and mentored
2 Associate Product Managers, driving delivery outcomes within an Agile product team.
Product Manager
Jar App – Bengaluru
November 2021 – September 2023
● Founding Product Manager for mandate-based automated investment products enabling daily
auto-debit investments within tightly regulated money-movement workflows.
● Designed and scaled recurring payment systems, driving a 25% increase in recurring automated
[CANDIDATE] BasuROHAN [CANDIDATE]
[EMAIL REDACTED] +91 98142 6031798142 60317 [CANDIDATE]-[CANDIDATE]-pm
transactions while improving mandate reliability and success rates.
● Owned onboarding and mandate setup journeys, reducing drop-offs in regulated consent and debit
flows through behavioral drop-off analysis and UX iteration.
● Led user research and structured experimentation driving a 109% increase in one-time investments.
● Established core product foundations from zero, defining product vision, roadmap, success metrics,
and backlog prioritization to support rapid scale.
● Instrumented key payment and investment events, reducing development rework by ~20% and
improving cross-team clarity.
Product Owner/Business Analyst
Forsys Inc. – Hyderabad
June 2020 – October 2021
● Owned Salesforce-based enterprise workflows, translating requirements into scalable cloud products.
● Led global stakeholder workshops and delivered Agile user stories with SQL-backed insights.
Software Engineer
Accenture – Pune
October 2014 – November 2017
● Built billing and invoicing systems for global telecom and B2B clients.
● Led automation initiatives reducing invoice disputes by 30%.
AI PRODUCT DEVELOPMENTS
Pathfinder — LLM-Powered Career Intelligence Platform (0→1)
February 2026 – Present
● Designed a 0→1 LLM-powered career intelligence platform that converts unstructured user inputs
into personalized career recommendations using structured AI reasoning.
● Designed confidence signaling and ambiguity-handling frameworks to improve trust in AI outputs,
while defining evaluation metrics for recommendation quality, clarity, and engagement.
Independent Product Exploration — AI Job Search Automation
September 2025 – January 2026
● Evaluated LLM latency, token cost, and reliability trade-offs in human-in-the-loop automation workflows.
● Identified scalability constraints in UI-dependent automation systems and made principled decisions
against high-maintenance architectures.
EDUCATION
MBA in Marketing — Great Lakes Institute of Management, Chennai | 2020
Bachelor of Technology — St. Thomas’ College of Engineering & Technology, Kolkata | 2014
Redacted: name ×4, 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, 11:37:03 AMsystemanalysis completed{"PM_score":55,"SPM_score":49}
  • 9/29/2026, 11:37:03 AMsystemrole assigned best fit{"role":"PM","PM_score":55,"SPM_score":49}
  • 9/29/2026, 11:29:35 AMsystemcv ingested{"file":"29_rohan_basu.pdf","role":"AUTO","redactions":{"name":4,"email":1,"profile_url":1},"name_detected":true}