Avinash Mukherjee

Applied: PMRank #33 of 45 PM applicantssquad_5@pg27.mesaschool.cono phone found25_avinash_mukherjee.pdf

Below the line

SPM

Below the line

PM rubric (applied role)36.0 / 100Below the line

Criterion	Score	Weight	Points	Evidence	Rationale
PM-1 Hands-on logistics operations experience	1/5	30%	6	none	No logistics or freight operations exposure is described in the CV; experience is in e-commerce growth/marketing algorithms and pharma manufacturing optimization.
PM-2 Builds from observed pain; peers adopt it	3/5	20%	12	equipped operator with a 3 faceted guide	Built an optimization pathway and guide for operators at Dr Reddy's, but there is no evidence of peer adoption across multiple self-started tools.
PM-3 Sole ownership with no layer in between	3/5	20%	12	POD Lead - Resurrection (Nov’25 – present)	Held POD Lead role and owned specific sub-areas/features within larger teams and initiatives as a Senior Associate.
PM-4 Discovery directly with operators	1/5	15%	3	none	No evidence of direct discovery interviews or qualitative user research conducted with operators or users.
PM-5 Candour: kills, post-mortems, owning what went wrong	1/5	15%	3	none	The CV presents a success-only narrative with no mention of kills, post-mortems, or failures.

SPM rubric (other role)30.0 / 100Below the line

Criterion	Score	Weight	Points	Evidence	Rationale
SPM-1 Hands-on logistics operations experience	1/5	25%	5	none	The CV contains marketing, growth, and pharmaceutical manufacturing experience, with no logistics operations exposure.
SPM-2 Senior-most owner, no one above them in the function	3/5	25%	15	POD Lead - Resurrection (Nov’25 – present)	Owned a specific POD area as a Senior Associate under higher functional management.
SPM-3 Turns fixes into standing practice	1/5	20%	4	none	There is no evidence of establishing standing operational or team practices.
SPM-4 Judgement under pressure, stated plainly	1/5	15%	3	none	The CV presents an entirely success-oriented narrative without describing hard calls, tradeoffs, or failures.
SPM-5 Grows people who then get promoted	1/5	15%	3	none	There is no mention of mentoring, coaching, or developing team members.

Rejection draft

draft

SubjectBody ([NAME] is replaced with the candidate's name when sent)

Redacted CV (exactly what the model 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
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