Dashboard / Candidate

Avinash Mukherjee

Placed in Senior Product Manager by best fit (no role stated; scored higher on this rubric) · 25_avinash_mukherjee.pdf

Private: squad_5@pg27.mesaschool.co · no phone found

Applied-role score

43/100

#9 of 16 SPM applicants

PM score

36/100

SPM score

43/100

Current status

Analysed

Below shortlist (AI suggestion)

Your decision

Why they fit

Problem -> Change -> Business/Operational Result 20%
The CV provides multiple concrete examples where the candidate's interventions led directly to quantified business and operational uplifts across GMV, NMV, and manufacturing cycle time. While outcomes are well documented, they primarily reflect tactical growth and model tuning rather than broader end-to-end product problem solving.
Evidence-Led Prioritisation and Reversal 20%
The candidate shows at least one clear example of observing data inefficiencies and pivoting focus from BAU optimizations to first-order optimizations. What is missing is evidence of stopping major initiatives or reversing wider strategic product roadmaps based on contrary findings.

Where they fall short

Independent Decision Ownership −18 pts
The candidate shows some initiative in redesigning cohorts and pivoting optimization strategies. However, the CV does not articulate the explicit trade-offs made, the degree of uncertainty, or evidence of making consequential decisions in the absence of a senior decision-maker.
Multi-Party Resolution Under Pressure −15 pts
Evidence is not present. The CV does not mention multi-stakeholder escalations, managing post-mortems, or navigating ambiguous, high-pressure team conflicts.
Creates Systems Others Run −12 pts
There is only indirect evidence consisting of equipping an operator with a guide during an internship. The CV provides no evidence of establishing operating rhythms, organizational frameworks, or repeatable product processes that were adopted and run by other team members.

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 concrete operational or targeting inefficiencies, implementing changes to cohorts, models, and processes, and measuring clear outcomes (e.g. 5% GMV/E uptick and 14.2% time reduction in coating). Multiple instances show a direct link between the candidate's action and visible results.	“Hypothesized the new structure basis observation of targeting & spending inefficiences in existing structure” “Pivoted from Platform 1oD+ BAU RMK optimizations to First Order optimizations for uninstalled userbase” “Delivered 5% GMV/E uptick in new structure vis-a-vis old structure, resulted in 0.15% Platform GMV/Vi uptick” “Saved 3.5 hrs- total 14.2% time reduction achieved through time-logged optimal pathway of processparameters”
Evidence-Led Decisions	20%	3/5	12.0/20	The CV shows evidence of pivoting strategy and scaling specific campaigns based on observed inefficiencies and experiment data. It clearly demonstrates the decision path and subsequent uplifts, though it focuses largely on quantitative marketing/algorithmic experimentation rather than post-mortems or customer feedback.	“Hypothesized the new structure basis observation of targeting & spending inefficiences in existing structure” “Pivoted from Platform 1oD+ BAU RMK optimizations to First Order optimizations for uninstalled userbase” “Scaled Google RMK on 2 cohorts (30M users), created further sub-cohorts based on DAU/ X Conversion insights”
Builds Repeatable Ways of Working	20%	1/5	4.0/20	There is minimal evidence of creating repeatable tools, templates, or review processes that others adopted. The CV mentions formulating an experimentation structure and providing a guide to an operator, but lacks evidence that these became reusable operating methods adopted across a team.	“Formulated the experimentation structure for Google RMK by cohortisation; aligned it with Meta RMK BAU” “Fulfilled acceptance criteria of 4 Critical Process Parameters (CPPs), equipped operator with a 3 faceted guide”
Carries Ambiguous Problems Through to Closure	20%	0/5	0.0/20	Evidence for this criterion is not present in the CV. The CV does not provide evidence of managing messy, cross-functional ambiguity, coordinating diverse stakeholders, or driving an escalation or failure through to completion and follow-up.	Evidence not present in the CV.
Gets to the Source Before Designing the Fix	15%	0/5	0.0/15	There is no evidence of directly interviewing, observing, or working alongside people or users experiencing a problem. Discovery is limited to automated data analysis and metric monitoring.	Evidence not present in the CV.
Total	100%		36	The candidate demonstrates strong quantitative problem-solving and growth experimentation skills, linking data-backed hypothesis testing directly to measurable GMV and efficiency uplifts. However, the CV lacks evidence for core product management criteria including firsthand user discovery, cross-functional ambiguity resolution, and creating repeatable ways of working for 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)

“Re-structured the existing model based on user cohortisation at App Inactivity (AI) to AI X Install_Status level” · Independent Decision Ownership
“Pivoted from Platform 1oD+ BAU RMK optimizations to First Order optimizations for uninstalled userbase” · Independent Decision Ownership
“Delivered 5% GMV/E uptick in new structure vis-a-vis old structure, resulted in 0.15% Platform GMV/Vi uptick” · Problem -> Change -> Business/Operational Result
“Delivered 0.10% Platform GMV/Vi uplift by scaling Google RMK on 30M users, while meeting IRR guardrails” · Problem -> Change -> Business/Operational Result
“Delivered 1.5% NMV/Vi uplift through Exploit Ranker at experimental level; scaled to the entire platform” · Problem -> Change -> Business/Operational Result
“Saved 3.5 hrs- total 14.2% time reduction achieved through time-logged optimal pathway of processparameters” · Problem -> Change -> Business/Operational Result
“Hypothesized the new structure basis observation of targeting & spending inefficiences in existing structure” · Evidence-Led Prioritisation and Reversal
“Pivoted from Platform 1oD+ BAU RMK optimizations to First Order optimizations for uninstalled userbase” · Evidence-Led Prioritisation and Reversal
“Scaled Google RMK on 2 cohorts (30M users), created further sub-cohorts based on DAU/ X Conversion insights” · Evidence-Led Prioritisation and Reversal
“equipped operator with a 3 faceted guide” · Creates Systems Others Run

Outcomes stated in the CV

Delivered 5% GMV/E uptick and 0.15% Platform GMV/Vi uptick through re-structured remarketing for a 75M resurrection user base at Meesho.
Achieved 50% lower incremental CAC on Sale Days through IVR campaigns supplemented with remarketing at Meesho.
Generated 0.10% Platform GMV/Vi uplift by expanding Google RMK to 30M users while adhering to IRR guardrails at Meesho.
Delivered 1.5% NMV/Vi uplift platform-wide via an Exploit Ranker on the FY Feed at Meesho.
Reduced tablet coating cycle time by 14.2% (3.5 hours) at Dr Reddy's Laboratories.
Achieved R2 = 0.76 and RMSE = 0.23 predicting surfactant logCMC using transformer models at IIT Kanpur.

Anonymised CV (exactly what the AI saw)

Major: Chemical Engineering
Academic Qualifications
Year Educational Qualification Institute CPI/%
2021 - 2025 Bachelor of Technology Indian Institute of Technology, Kanpur 8.3/10.0
2021 Class XII (CBSE) GVN The Global School, Bhopal 95.2%
2019 Class X (CBSE) Delhi Public School, Bhopal 97.0%
Work Experience
Meesho | Senior Associate (Jun’25 - present)
POD Lead - Resurrection (Nov’25 – present)
Objective ● Drive GMV incrementality through re-activation of non-transacted users in the past >= 180 days (180D+ OI)
● Structure & tinker the remarketing (RMK) effort for the re-acquisition strategy of this ~75M Resurrection Base
Approach
● Re-structured the existing model based on user cohortisation at App Inactivity (AI) to AI X Install_Status level
● Hypothesized the new structure basis observation of targeting & spending inefficiences in existing structure
● Pivoted from Platform 1oD+ BAU RMK optimizations to First Order optimizations for uninstalled userbase
● Experimented and proved GMV incrementality on ~30M userbase, further-on scaled it to the entire ~75M Base
● Identified the most incremental Region X Gender cohorts in RCS & IVR campaigns executed during Sale days
● Scaled IVR campaigns on Female MAU Base, with incrementality holding beyond first week post Sale (baseline)
Impact ● Delivered 5% GMV/E uptick in new structure vis-a-vis old structure, resulted in 0.15% Platform GMV/Vi uptick
● Achieved a 50% lower incremental CAC on Sale Days by incorporating IVR campaigns supplemented with RMK
Google & Meta led Remarketing POD (Nov’25 – present)
Objective ● Drive NMV incrementality, 1st Order activations (1oA) uplift by introducing Google Ads as an RMK channel
● Maintain & boost DAU/E, GMV/E uplifts from Meta RMK BAU effort while improving Internal Rate of Return(IRR)
Approach
● Formulated the experimentation structure for Google RMK by cohortisation; aligned it with Meta RMK BAU
● Launched 20+ experiments to figure out the optimum Burn/E X Campaign Type X Google/Meta Spends X cohort
● Scaled Google RMK on 2 cohorts (30M users), created further sub-cohorts based on DAU/ X Conversion insights
● Observed 5% GMV/E uptick in 1 sub-cohort (platform userbase of 60M) at experimentation stage on 6M users
● Identified Meta RMK opportunities for Sale Days, launched 3 booster campaigns on combined userbase of 150M
Impact ● Delivered 0.10% Platform GMV/Vi uplift by scaling Google RMK on 30M users, while meeting IRR guardrails
● Achieved 1% GMV/E uptick for 150M userbase through combination of Meta RMK boosters on Sale Days
Homepage POD (Jun’25 – Nov’25)
Objective ● Drive NMV incrementality by optimizing FY Feed, supplemented with increased retention (DAU/MAU) metrics
● Boost feed performance through rankers; capture seasonal trends and serve user relevant trend X catalogs
Approach
● Served catalogs in FY Feed through bifurcation into a variety of explore & exploit CGs based on past user intent
● Built 2 layer ranker stack–first having explore & exploit CG ranker; second predicting explore/exploit at App Open
● Defined user, cohort, platform and momentum ranker features directly aligned with CG level use-cases & nuances
● Created a 3 stage cohort-based trend identification pipeline; trend identification via PST spike (3 DMA/30DMA)
● Integrated & tuned LLMs to map PSTs to seasonal themes, followed by relevance check to filter out random noise
● Generated optimized theme X catalog matrix; user X theme boosting/deboosting using CTR based Wilson score
Impact ● Delivered 1.5% NMV/Vi uplift through Exploit Ranker at experimental level; scaled to the entire platform
● Achieved 0.3% NMV/Vi uplift at 20% platform exposure through Seasonal CG, captured 95% of local trends
Research Internships
Dr Reddy’s Laboratories | Young Leaders’ Program Technical Intern (May’24-Jul’24)
Objective ● Reduce functional coating time of Pantoprazole 40mg DR tablets, spanning 25 hours in the manufacturing plant
Approach
● Segmented 12K+ parameter-weight gain-time datapoints to characterise 70+ environments in pan coater
● Constructed a 21.25 hour optimal pathway by generating environments which maximise coating at each minute
● Validation through examination of 150+ parameter-weight gain-time harmonisations of result & source data
Impact ● Saved 3.5 hrs- total 14.2% time reduction achieved through time-logged optimal pathway of processparameters
● Fulfilled acceptance criteria of 4 Critical Process Parameters (CPPs), equipped operator with a 3 faceted guide
Surfactant Property Prediction & De-Novo Design using Transformers | UG Project, IIT Kanpur (Aug’24 – Dec’24)
Objective ● Leverage transformer-based models to generate optimized surfactants & predict logCMC using SMILES strings
Approach ● Curated source data of 427 surfactants with logCMC; preprocessed using tokenization, normalization & encoding
● Trained generative model on 90,951 molecule pairs; accomplished L1 & L3 levels of transformer integration
Impact ● Achieved R2 = 0.76, RMSE = 0.23 on logCMC prediction; validated clustering via t-SNE & enabled de-novo design
Skills
● Product Management: Product Thinking, A/B Testing, Root Cause Analysis (RCA), Product Design, SQL
● Programming: C, C++, Python | Libraries: Numpy, Pandas, Matplotlib, Scikit-learn, TensorFlow | Misc: Excel, Latex
[CANDIDATE] MukherjeeAVINASH [CANDIDATE] [EMAIL REDACTED]
+91 99581 7203499581 72034

Redacted: name ×2, email ×1

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Audit trail (3)

9/29/2026, 11:36:51 AMsystemanalysis completed{"PM_score":36,"SPM_score":43}
9/29/2026, 11:36:51 AMsystemrole assigned best fit{"role":"SPM","PM_score":36,"SPM_score":43}
9/29/2026, 11:29:34 AMsystemcv ingested{"file":"25_avinash_mukherjee.pdf","role":"AUTO","redactions":{"name":3,"email":1},"name_detected":true}