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Dashboard / Candidate

Shrey Marathe

Placed in Product Manager by best fit (no role stated; scored higher on this rubric) · 24_shrey_marathe.pdf
Private: squad_5@pg27.mesaschool.co · no phone found
Applied-role score
80/100
#1 of 34 PM applicants
PM score
80/100
SPM score
43/100
Current status
SelectedSent
Recommend shortlist (AI suggestion)
Your decision
1

Why they fit

  • Problem -> Change -> Result 25%

    The candidate provides multiple strong, direct examples of identifying friction or operational problems, implementing product or workflow changes, and measuring clear outcomes (e.g., boosting CRM bot active users from 3 to 200, achieving an 18% conversion lift on listings, and cutting $20K annually by replacing third-party software).

  • Builds Repeatable Ways of Working 20%

    The CV shows the candidate built an internal ticketing tool from concept to production that replaced Suptask and became the operational standard, saving $20K annually. Additionally, they designed reusable discussion formats scaled across 30+ sessions.

  • Gets to the Source Before Designing the Fix 15%

    The CV highlights repeated, strong examples of firsthand user discovery directly driving product changes. The candidate shadowed 20+ users to identify workflow friction for internal tooling and interviewed 70+ users to pinpoint Custom UPI drop-offs, resulting in measurable business improvements.

2

Where they fall short

No criterion scored 2/5 or below.

3

Rubric breakdown: PM

PM (applied)SPM
CriterionWeightScoreWeightedAI reasoningEvidence from CV
Problem -> Change -> Result25% 5/525.0/25The candidate provides multiple strong, direct examples of identifying friction or operational problems, implementing product or workflow changes, and measuring clear outcomes (e.g., boosting CRM bot active users from 3 to 200, achieving an 18% conversion lift on listings, and cutting $20K annually by replacing third-party software).
  • “Improved conversions for low-performing property listings by diagnosing property images as a major drop-off driver and optimizing UI using user behaviour insights, resulting in a 18% lift in booking conversions- worked closely with data and inventory teams.”
  • “Shipped an internal ticketing tool from concept to production by defining product flows and validating a production-ready POC with engineering; replaced a third-party solution (Suptask) and saved ~$20K annually.”
  • “Improved adoption of an internal CRM chatbot by shadowing 20+ users to understand daily workflows, identifying key friction points around bot placement, performance, and accuracy, and shipping targeted fixes that increased active usage from 3 to 200 users.”
Evidence-Led Decisions20% 3/512.0/20The candidate demonstrates evidence-led prioritisation by diagnosing specific drop-off drivers using user behaviour insights and qualitative data before shipping targeted solutions. While it clearly shows data shaping the interventions and resulting in positive metrics, there is less evidence of pausing, stopping, or reversing an existing decision based on evidence.
  • “Ran A/B experiments for a revenue project “missions”, by testing varied UI copies that led to 4x more user adoption & 3x increase in revenue.”
  • “Improved conversions for low-performing property listings by diagnosing property images as a major drop-off driver and optimizing UI using user behaviour insights, resulting in a 18% lift in booking conversions- worked closely with data and inventory teams.”
Builds Repeatable Ways of Working20% 4/516.0/20The CV shows the candidate built an internal ticketing tool from concept to production that replaced Suptask and became the operational standard, saving $20K annually. Additionally, they designed reusable discussion formats scaled across 30+ sessions.
  • “Shipped an internal ticketing tool from concept to production by defining product flows and validating a production-ready POC with engineering; replaced a third-party solution (Suptask) and saved ~$20K annually.”
  • “Built an offline discussion community focused on under-explored and polarized topics; drove 5→600+ attendee participation by designing structured discussion formats across 30+ monthly Raडा sessions.”
Carries Ambiguous Problems Through to Closure20% 3/512.0/20The candidate demonstrates driving ambiguous execution to closure by identifying and resolving 80+ issues for an app release, supporting rollout strategy, and establishing post-launch monitoring dashboards. Specific detail on navigating cross-functional conflict or handling major escalations is limited.
  • “Drove release execution of a new web-view mobile app, identifying and closing 80+ issues, supporting rollout strategy, and setting up dashboards to monitor post-launch health.”
Gets to the Source Before Designing the Fix15% 5/515.0/15The CV highlights repeated, strong examples of firsthand user discovery directly driving product changes. The candidate shadowed 20+ users to identify workflow friction for internal tooling and interviewed 70+ users to pinpoint Custom UPI drop-offs, resulting in measurable business improvements.
  • “Spoke to 70+ users to diagnose drop-offs on the Custom UPI feature, that lead to UX tweaks driving a 3x revenue lift.”
  • “Improved adoption of an internal CRM chatbot by shadowing 20+ users to understand daily workflows, identifying key friction points around bot placement, performance, and accuracy, and shipping targeted fixes that increased active usage from 3 to 200 users.”
Total100%80The candidate displays strong alignment with the PM rubric, particularly in firsthand discovery, problem-to-result execution, and building internal tooling adopted by teams. While they have solid examples of driving releases to closure and using data to guide UI interventions, providing clearer evidence of stopping or reversing decisions based on post-mortems or unexpected data would further strengthen their profile.
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 conversions for low-performing property listings by diagnosing property images as a major drop-off driver and optimizing UI using user behaviour insights, resulting in a 18% lift in booking conversions- worked closely with data and inventory teams.” · Problem -> Change -> Result
  • “Shipped an internal ticketing tool from concept to production by defining product flows and validating a production-ready POC with engineering; replaced a third-party solution (Suptask) and saved ~$20K annually.” · Problem -> Change -> Result
  • “Improved adoption of an internal CRM chatbot by shadowing 20+ users to understand daily workflows, identifying key friction points around bot placement, performance, and accuracy, and shipping targeted fixes that increased active usage from 3 to 200 users.” · Problem -> Change -> Result
  • “Ran A/B experiments for a revenue project “missions”, by testing varied UI copies that led to 4x more user adoption & 3x increase in revenue.” · Evidence-Led Decisions
  • “Improved conversions for low-performing property listings by diagnosing property images as a major drop-off driver and optimizing UI using user behaviour insights, resulting in a 18% lift in booking conversions- worked closely with data and inventory teams.” · Evidence-Led Decisions
  • “Shipped an internal ticketing tool from concept to production by defining product flows and validating a production-ready POC with engineering; replaced a third-party solution (Suptask) and saved ~$20K annually.” · Builds Repeatable Ways of Working
  • “Built an offline discussion community focused on under-explored and polarized topics; drove 5→600+ attendee participation by designing structured discussion formats across 30+ monthly Raडा sessions.” · Builds Repeatable Ways of Working
  • “Drove release execution of a new web-view mobile app, identifying and closing 80+ issues, supporting rollout strategy, and setting up dashboards to monitor post-launch health.” · Carries Ambiguous Problems Through to Closure
  • “Spoke to 70+ users to diagnose drop-offs on the Custom UPI feature, that lead to UX tweaks driving a 3x revenue lift.” · Gets to the Source Before Designing the Fix
  • “Improved adoption of an internal CRM chatbot by shadowing 20+ users to understand daily workflows, identifying key friction points around bot placement, performance, and accuracy, and shipping targeted fixes that increased active usage from 3 to 200 users.” · Gets to the Source Before Designing the Fix
Outcomes stated in the CV
  • Achieved 4x user adoption and 3x revenue increase through A/B testing at Fampay.
  • Drove a 3x revenue lift via UX optimizations on Fampay Custom UPI.
  • Onboarded the first 3,000 active users for NamasPay through early GTM execution.
  • Scaled university white-label solution across 30+ pages with ~50% conversion and zero paid CAC at amberstudent.
  • Delivered an 18% lift in booking conversions for underperforming listings at amberstudent.
  • Saved ~$20K annually by replacing third-party software with an in-house ticketing tool.
  • Grew internal CRM chatbot active usage from 3 to 200 users at amberstudent.
  • Scaled community attendance from 5 to 600+ participants across 30+ sessions at Raडा.
Anonymised CV (exactly what the AI saw)
Product & Growth
Fampay | June 2023 - Sept 2023
Product Associate with 3+ years of experience in B2C. I maximize my impact on driving product scalability and business growth by solving user
problems while balancing revenue and operational impact. I work through systematic problem-solving frameworks, qualitative research, and
close collaboration with cross-functional teams. I’m currently looking to apply these skills in a high-growth company environment.
* Product Associate
Ran A/B experiments for a revenue project “missions”, by testing varied
UI copies that led to 4x more user adoption & 3x increase in revenue.

Spoke to 70+ users to diagnose drop-offs on the Custom UPI feature,
that lead to UX tweaks driving a 3x revenue lift.

Led early GTM execution for NamasPay (UPI for foreigners) by
identifying acquisition channels and building partnerships with travel
aggregators, helping onboard the first 3,000 active users.
Community Experience Director & Founder
Raडा | June 2020 - Aug 2024
Built an offline discussion community focused on under-explored and
polarized topics; drove 5→600+ attendee participation by designing
structured discussion formats across 30+ monthly Raडा sessions.
Product & Business Operations
Freggie Fermentary | June 2020 - May 2024
Built and scaled a food D2C brand, making ₹1L in profits; owning
manufacturing, supply chain, pricing, and P&L; reduced costs by ~50%
and launched 8 products.
Product Associate
amberstudent | March 2024 - current
Led research, discovery, and PRD for a University white-label solution by
identifying university websites as a key decision-making touchpoint for
students; scaled it across 30+ university pages, unlocking a new
acquisition channel with ~50% user conversion and zero paid CAC.

Improved conversions for low-performing property listings by
diagnosing property images as a major drop-off driver and optimizing UI
using user behaviour insights, resulting in a 18% lift in booking
conversions- worked closely with data and inventory teams.

Shipped an internal ticketing tool from concept to production by defining
product flows and validating a production-ready POC with engineering;
replaced a third-party solution (Suptask) and saved ~$20K annually.

Led migration of key content sections from CSR to SSR, optimizing page
load time, improved SEO crawlability & page rankings as a PLG project.

Improved adoption of an internal CRM chatbot by shadowing 20+ users
to understand daily workflows, identifying key friction points around bot
placement, performance, and accuracy, and shipping targeted fixes that
increased active usage from 3 to 200 users.
Drove release execution of a new web-view mobile app, identifying and
closing 80+ issues, supporting rollout strategy, and setting up dashboards
to monitor post-launch health.
M ixPanel Analytics certification

Technology for Product Managers

Business Development foundations
2019-2023 | MIT ADT University, Pune

B.Tech. Mechatronics & Automation
Experience Design
Gut Health Communication
Product Design
Google sheets Tableau Figma JIRA Notion
Skills
Certifications
Tools
Education
Interests
|
Product Lifecycle Management: Roadmaps, Agile
systems, & Go-to-Market Strategy (GTM).

User Experience & Design: User Research (Qualitative &
Quantitative) & Prototyping MVPs.

Cross-functional Collaboration: Stakeholder
Management, & Team Facilitation.

Written Communication: PRDs, & User stories.
Keynote Speaker at MIT SOE on Product Management

Speaker at TEDx VIT Pune and TEDx PVG COET
Keynote speaker at Pune Rotary club
Keynote speaker at MIT orientation (
on
fermentation & gut health.

on Allyship &
Inclusivity.

5,000+ attendees)

Featured on Zee24 Taas for an interview on inclusivity.

Wrote articles for Sakal, TOI, & Femina Magazine.

3 times trophy winner at ADT Talks; oratory
championship.

Won 2nd prize in amber hackathon- Built a Property
Comparison Tool.
Achievements
[CANDIDATE] MaratheShrey [CANDIDATE]
[EMAIL REDACTED] +91 97182 6043797182 60437
[CANDIDATE]-[CANDIDATE]
Redacted: name ×7, email ×1
5

Interview brief

The candidate shows exceptional strengths in direct user discovery and connecting problem diagnosis to tangible outcomes, such as increasing CRM bot usage from 3 to 200 users and cutting $20K in software costs. They demonstrate solid ability in standardizing operational workflows and driving execution through launch. However, they fall short on demonstrating evidence-led decisions that involve stopping or pivoting failed initiatives, and show limited evidence of navigating contentious stakeholder conflicts or strategic ambiguity.

Strongest evidence

Improved adoption of an internal CRM chatbot by shadowing 20+ users to understand daily workflows, identifying key friction points around bot placement, performance, and accuracy, and shipping targeted fixes that increased active usage from 3 to 200 users.

Biggest area to probe

Evidence-Led Decisions (weight 20%): The rubric assessment highlights a lack of evidence where disconfirming data led to pausing, pivoting, or killing a project. The interview must probe whether the candidate can evaluate negative signals objectively or if they focus solely on finding incremental optimizations.

Important uncertainty

Whether the candidate has experience managing difficult stakeholder alignment, trade-offs, or failed hypotheses independently, rather than executing straightforward feature optimizations and bug resolution.

Risks
  • Evidence-Led Decisions: May default to optimizing surface-level elements (e.g., UI copy or images) instead of rethinking core product viability when evidence reveals fundamental flaws.
  • Carries Ambiguous Problems Through to Closure: Bug triaging on the web-view app indicates tactical task tracking, which may not translate to resolving ambiguous or contentious stakeholder trade-offs.
Suggested interview questions
  1. Can you walk me through a time when data or user feedback showed an initiative you believed in was failing, and you chose to kill or radically pivot it?
    Tests Evidence-Led Decisions to verify whether the candidate can make objective descope/kill decisions rather than only reporting positive A/B tests.
  2. When driving the release execution for the new web-view mobile app and resolving 80+ issues, how did you decide which issues to cut or defer, and how did you resolve conflicts with engineering or business stakeholders over those cuts?
    Tests Carries Ambiguous Problems Through to Closure by evaluating how the candidate navigates cross-functional conflict and prioritization under pressure.
  3. When shadowing the 20+ users for the CRM chatbot, what unexpected finding contradicted your initial assumptions, and what planned features did you decide NOT to build based on that discovery?
    Verifies Gets to the Source Before Designing the Fix and probes whether discovery actively shaped scope reduction.
  4. When replacing Suptask with an internal ticketing tool, what resistance did you face from internal teams accustomed to the old workflow, and how did you ensure adoption across departments?
    Tests Builds Repeatable Ways of Working and change management when shifting organizational processes.
  5. For the 'missions' project where you ran A/B tests on UI copies, what deeper product or behavioural levers did you investigate before deciding copy optimization was the right approach?
    Probes Evidence-Led Decisions and Problem -> Change -> Result to determine if the candidate relies too heavily on low-effort copy tweaks over deeper structural product changes.
6

Email draft

To: squad_5@pg27.mesaschool.co
Offer of Employment: Product Manager at Kargo
Dear Shrey,

I am thrilled to formally offer you the Product Manager role at Kargo. Our entire team thoroughly enjoyed our conversations and came away energized by your vision.

In particular, your dedication to getting to the source before designing a fix stood out to us. Your consistent emphasis on firsthand user discovery—such as shadowing users to identify internal workflow friction and interviewing dozens of users to pinpoint drop-offs—demonstrates the exact customer-centric, analytical rigor we need here in Mumbai.

The formal offer letter containing your compensation, start date, and joining details will follow shortly. In the meantime, please reply directly to this email to confirm your interest so we can schedule a quick call to connect.

Arjun Mehta
Founder, Kargo
Status: sent · 10/2/2026, 7:57:53 AM
Audit trail (8)
  • 10/2/2026, 7:57:54 AMfounderemail sent{"type":"offer","draft_id":"5b159964-ebe7-4b17-abe0-7777adac9111","test_recipient":true}
  • 10/2/2026, 7:57:28 AMsystemoffer draft generated{"draft_id":"5b159964-ebe7-4b17-abe0-7777adac9111"}
  • 10/2/2026, 7:57:17 AMfounderselected for offer{"batch_size":1}
  • 9/29/2026, 11:39:16 AMsystemassessment generated{"model":"gemini:gemini-flash-latest"}
  • 9/29/2026, 11:37:08 AMsystemauto finalist{"role":"PM","score":80}
  • 9/29/2026, 11:36:44 AMsystemanalysis completed{"PM_score":80,"SPM_score":43}
  • 9/29/2026, 11:36:43 AMsystemrole assigned best fit{"role":"PM","PM_score":80,"SPM_score":43}
  • 9/29/2026, 11:29:33 AMsystemcv ingested{"file":"24_shrey_marathe.pdf","role":"AUTO","redactions":{"name":7,"email":1},"name_detected":true}