Dashboard / Candidate

Deepika Nair

Applied for Product Manager · pm_03_deepika_nair.pdf

Private: squad_3@pg27.mesaschool.co · +91 94478 20391

Applied-role score

56/100

#7 of 34 PM applicants

PM score

56/100

SPM score

48/100

Current status

Analysed

Borderline (AI suggestion)

Your decision

Why they fit

Problem -> Change -> Result 25%
The candidate provides a clear example of identifying a friction point and implementing a solution with visible adoption, as well as delivering measurable efficiency gains in their PM role. Specifically, they designed an extraction engine that reduced processing time from 45 to 6 minutes, and created a tracker that became the team's primary tool.
Builds Repeatable Ways of Working 20%
The candidate has one clear example of creating a tool that was adopted by others as an ongoing operational method. Missing are repeated examples or formalisation of processes within the product management role itself.
Gets to the Source Before Designing the Fix 15%
The candidate spent 6 weeks in the field across 4 offices directly observing real documents, using those firsthand operational learnings to design the core extraction rules engine rather than relying on spec sheets.

Where they fall short

Carries Ambiguous Problems Through to Closure −16 pts
The candidate notes routine operational coordination across ports, banks, and buyers, but does not present an example of driving a messy, ambiguous failure or issue across functions through to post-resolution closure.
Evidence-Led Decisions −12 pts
The CV shows that iterations were prioritised based on edge cases discovered in production and client feedback rather than a pre-set roadmap. However, it lacks specific detail on the reasoning behind changing or stopping a particular decision and what concrete result followed.

Rubric breakdown: PM

PM (applied)SPM

Criterion	Weight	Score	Weighted	AI reasoning	Evidence from CV
Problem -> Change -> Result	25%	4/5	20.0/25	The candidate provides a clear example of identifying a friction point and implementing a solution with visible adoption, as well as delivering measurable efficiency gains in their PM role. Specifically, they designed an extraction engine that reduced processing time from 45 to 6 minutes, and created a tracker that became the team's primary tool.	“Reduced average BoL processing time at the company's pilot client from 45 minutes to 6 minutes per document” “Built a shipment status tracker in Excel after noticing that the team had no reliable way to tell clients where a shipment stood; it became the team's primary client communication tool”
Evidence-Led Decisions	20%	2/5	8.0/20	The CV shows that iterations were prioritised based on edge cases discovered in production and client feedback rather than a pre-set roadmap. However, it lacks specific detail on the reasoning behind changing or stopping a particular decision and what concrete result followed.	“Shipped the first working version in 10 weeks; iterated 4 times in 6 months based on edge cases discovered in production — each iteration triggered by a client conversation, not a roadmap plan”
Builds Repeatable Ways of Working	20%	3/5	12.0/20	The candidate has one clear example of creating a tool that was adopted by others as an ongoing operational method. Missing are repeated examples or formalisation of processes within the product management role itself.	“Built a shipment status tracker in Excel after noticing that the team had no reliable way to tell clients where a shipment stood; it became the team's primary client communication tool”
Carries Ambiguous Problems Through to Closure	20%	1/5	4.0/20	The candidate notes routine operational coordination across ports, banks, and buyers, but does not present an example of driving a messy, ambiguous failure or issue across functions through to post-resolution closure.	“Coordinated with JNPT, Kochi Port Trust, and Air Cargo Complex at BIA across both import and export clearances” “Managed LC document preparation for 8 regular exporter clients — coordinated with negotiating banks and buyers' agents directly”
Gets to the Source Before Designing the Fix	15%	4/5	12.0/15	The candidate spent 6 weeks in the field across 4 offices directly observing real documents, using those firsthand operational learnings to design the core extraction rules engine rather than relying on spec sheets.	“Designed the extraction rules engine after 6 weeks of field work at 4 customs house agent offices in Kochi, Chennai, and Mumbai; built rules from real documents, not specification sheets”
Total	100%		56	The candidate demonstrates strong user research and problem-to-result execution, particularly through hands-on fieldwork and measurable operational improvements. They show basic evidence of building adopted tools and iterating via user feedback, but lack documentation of managing ambiguous cross-functional crises to 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.

Evidence

Verified CV quotes used (PM rubric)

“Reduced average BoL processing time at the company's pilot client from 45 minutes to 6 minutes per document” · Problem -> Change -> Result
“Built a shipment status tracker in Excel after noticing that the team had no reliable way to tell clients where a shipment stood; it became the team's primary client communication tool” · Problem -> Change -> Result
“Shipped the first working version in 10 weeks; iterated 4 times in 6 months based on edge cases discovered in production — each iteration triggered by a client conversation, not a roadmap plan” · Evidence-Led Decisions
“Built a shipment status tracker in Excel after noticing that the team had no reliable way to tell clients where a shipment stood; it became the team's primary client communication tool” · Builds Repeatable Ways of Working
“Coordinated with JNPT, Kochi Port Trust, and Air Cargo Complex at BIA across both import and export clearances” · Carries Ambiguous Problems Through to Closure
“Managed LC document preparation for 8 regular exporter clients — coordinated with negotiating banks and buyers' agents directly” · Carries Ambiguous Problems Through to Closure
“Designed the extraction rules engine after 6 weeks of field work at 4 customs house agent offices in Kochi, Chennai, and Mumbai; built rules from real documents, not specification sheets” · Gets to the Source Before Designing the Fix

Outcomes stated in the CV

Reduced average BoL processing time at pilot client from 45 minutes to 6 minutes per document
Brought in 2 additional CHA clients through product demos directly to ops heads
Shipped the first working version of freight document automation product in 10 weeks and iterated 4 times in 6 months based on edge cases in production
Built a shipment status tracker in Excel that became the team's primary client communication tool

Anonymised CV (exactly what the AI saw)

[CANDIDATE]
[PHONE REDACTED] · [EMAIL REDACTED] · Kochi
[PROFILE URL REDACTED]
PROFESSIONAL SUMMARY
Product Manager with roots in customs documentation and freight forwarding operations. Ran operations at a
family CHA firm for 3 years before completing an MBA and moving into product. Built and owned a freight
document automation product from scratch at an early-stage startup. Comfortable with the language and the reality
of import-export operations — not just the theory.
WORK EXPERIENCE
Product Manager – Document Automation
ClearDoc Technologies Pvt. Ltd. (Freight-Tech, 20 employees), Bengaluru · Jun 2022 – Present
— Owns the core document processing product — Bill of Lading extraction, shipping instruction validation, and
customs filing automation — sole PM from day one
— Designed the extraction rules engine after 6 weeks of field work at 4 customs house agent offices in Kochi,
Chennai, and Mumbai; built rules from real documents, not specification sheets
— Shipped the first working version in 10 weeks; iterated 4 times in 6 months based on edge cases discovered in
production — each iteration triggered by a client conversation, not a roadmap plan
— Reduced average BoL processing time at the company's pilot client from 45 minutes to 6 minutes per document
— Brought in 2 additional CHA clients through product demos; no sales team at the time — presented the product
myself to ops heads
Operations Executive
[CANDIDATE] CHA & Freight Services, Kochi · Jul 2016 – May 2019
— Handled Bill of Lading preparation, shipping instructions, customs clearance coordination, and bank documentation
for 120–150 shipments monthly
— Coordinated with JNPT, Kochi Port Trust, and Air Cargo Complex at BIA across both import and export clearances
— Managed LC document preparation for 8 regular exporter clients — coordinated with negotiating banks and buyers'
agents directly
— Built a shipment status tracker in Excel after noticing that the team had no reliable way to tell clients where a
shipment stood; it became the team's primary client communication tool
EDUCATION
MBA, Operations & Strategy · Great Lakes Institute of Management, Chennai · 2020–2022 · Gold Medalist,
Operations
B.Com · St. Teresa's College, Kochi · 2013–2016 · First Division
SKILLS & CERTIFICATIONS
Product management · Customs documentation · Bill of Lading processing · JIRA · Figma · SQL · Python (basic) ·
CargoWise · ICEGATE
Certified Product Manager – AIPMM (2023)

Redacted: name ×2, email ×1, phone ×1, profile url ×1

Interview brief

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Email draft

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Audit trail (2)

9/29/2026, 11:02:08 AMsystemanalysis completed{"PM_score":56,"SPM_score":48}
9/29/2026, 11:01:24 AMsystemcv ingested{"file":"pm_03_deepika_nair.pdf","role":"PM","redactions":{"name":2,"email":1,"phone":1,"profile_url":1},"name_detected":true}