Open to Work — Data & Product Roles

Rashmi
Data Analyst
& PM

Transforming raw, messy data into strategic decisions. I bridge the gap between stakeholder needs, analytical rigour, and actionable product insights.

Python SQL Tableau Power BI Excel Product Strategy A/B Testing Stakeholder Mgmt
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Rashmi
Data Analyst
Python · SQL · Tableau
Product Manager
Strategy · KPIs · PRDs
Open to Work
Available Immediately
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Case Studies
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Records Analyzed
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Key Insights
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Tools Mastered
R
Data Analysis90%
SQL & Python85%
Tableau / BI80%
Product Thinking88%
Stakeholder Comm.92%
About Me

Data-driven. Product-minded. Impact-focused.

I am a data analyst with a product manager's mindset. I don't just clean datasets and produce charts — I ask why the data looks the way it does, who it affects, and what decision it should drive.

My work spans two high-impact domains: understanding why startups fail and mapping the great tech layoff wave. Both projects combine rigorous data engineering, clear KPI frameworks, and visual storytelling.

I am actively looking for data analyst, BI analyst, or associate PM roles where I can marry analytical depth with product instinct.

Startup Ecosystem Tech Industry India · Global B.Tech Student
Case Studies
Deep-Dive Project Work
Drag the project image downward to reveal the full case study. Each follows a rigorous PM + DA framework from data ingestion to board-ready insights.
42% 29% 23%
Case Study 01 · Startup Ecosystem Analysis
Why Startups Fail
A data-driven post-mortem across industries, stages, and geographies — surfacing the systemic patterns that venture capital and founders must confront.
Python Pandas Matplotlib Tableau Excel Statistical Analysis
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Problem Statement & Stakeholder Context

90% of startups fail within 10 years — but the reasons are poorly understood, often anecdotally explained as "bad timing" or "wrong market." This analysis was commissioned to give investors, accelerators, and early-stage founders an evidence-based failure taxonomy. Stakeholders included startup incubators evaluating due-diligence frameworks, and student founders refining their MVP strategies.

Dataset Source & Scope
  • Primary: Curated startup post-mortem database (Crunchbase, CB Insights, AngelList)
  • Scope: 1,200+ startup records across 2008–2023
  • Fields: Funding stage, sector, founding team size, burn rate, pivot history, failure reason tags
  • Geography: US (60%), India (20%), EU (15%), Other (5%)
Cleaning & Transformation Summary
  • Removed 340 duplicate and incomplete records — final clean set: 910 rows
  • Standardised 47 failure-reason tags into 8 canonical categories using NLP clustering
  • Imputed missing funding data using stage-median values
  • Created derived columns: survival_months, funding_efficiency_ratio, team_diversity_score
  • Normalised sector labels across 3 naming conventions
KPI Framework
TTF
Time-to-Failure (months)
FER
Funding Efficiency Ratio
CFR
Category Failure Rate %
PMF
Product-Market Fit Score
Key Insights
INSIGHT 01 — No Market Need42% of failed startups cited no market need as the primary reason — dwarfing cash flow issues (29%) and team problems (23%).
INSIGHT 02 — The 18-Month Cliff68% of failures occurred within the first 18 months, peaking at months 12–15 when seed runway typically dries up.
INSIGHT 03 — Solo Founder RiskSolo-founder startups failed 2.3x more often than co-founder teams with complementary skill sets.
INSIGHT 04 — Pivot ParadoxStartups that pivoted once increased survival odds by 40%, but multiple pivots (3+) correlated with accelerated failure within 6 months.
INSIGHT 05 — Fintech ResilienceDespite highest burn rates, FinTech startups showed 30% better survival rates than SaaS — attributed to regulatory moats and B2B contract lock-in.
Dashboard Preview
Market
Cash
Team
Comp.
Price
Timing
View Full Dashboard on GitHub →
Recommendations & Expected Impact
1
Pre-PMF Validation GateIncubators should mandate a customer discovery sprint before seed funding — projected to reduce no-market-need failures by 25%.
2
Co-founder Matching ProgramsAccelerators should facilitate T-shaped co-founder matching — estimated 30% improvement in early survival rates.
3
12-Month Runway AuditsInvestors should trigger operational reviews at month 10, before the failure cliff at months 12–15.
910
Clean Records
42%
Top Failure Cause
2.3x
Solo Founder Risk
8
Failure Categories
15yr
Data Span
2020 2021 2022 2023 2024 Peak
Case Study 02 · Tech Industry Workforce Analysis
The Great Tech Layoff Wave
Mapping 2020–2024's unprecedented tech layoff cycle: who was cut, when, why, and what it signals for the future of the tech workforce and hiring strategy.
Python SQL Tableau NumPy Seaborn Time-Series Analysis
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Problem Statement & Stakeholder Context

After pandemic-era over-hiring, Big Tech shed 400,000+ jobs between 2022–2024. This project answers: Are these layoffs random cost-cuts or a structural reset? Stakeholders include HR leaders benchmarking workforce strategy, job-seekers navigating the market, and investors interpreting operational efficiency signals. The analysis provides a sector-level lens on which functions and companies were hardest hit.

Dataset Source & Scope
  • Source: Layoffs.fyi (crowd-sourced), LinkedIn Workforce Reports, SEC filings
  • Records: 3,200+ layoff events across 2020–2024
  • Fields: Company, total_laid_off, percentage_laid_off, date, industry, stage, country, funds_raised
  • Spans: 50+ countries, 15 tech sub-sectors
Cleaning & Transformation Summary
  • Handled 18% missing values in percentage_laid_off via company headcount cross-reference
  • Standardised date formats across 4 regional conventions
  • Created rolling_30d_layoffs and cumulative_impact columns for time-series analysis
  • Classified 3,200 events into 6 layoff_type clusters: restructuring, post-IPO correction, AI pivot, cost efficiency, market contraction, acquisition
  • Removed 120 duplicate announcements
KPI Framework
LVR
Layoff Volume Rate
SPI
Sector Pressure Index
RRI
Recovery Rate Index
CFR
Capital-to-Firing Ratio
Key Insights
INSIGHT 01 — Q1 2023: The EpicentreJan–Mar 2023 saw the single largest layoff wave: 150,000+ jobs in 90 days. Amazon, Google, Microsoft, and Meta accounted for 38% of this alone.
INSIGHT 02 — Crypto Imploded FirstThe crypto/Web3 sector began contracting 6 months before broader tech — a leading indicator that risk appetite had peaked.
INSIGHT 03 — AI Pivot EffectCompanies that announced AI initiatives within 3 months of layoffs raised 40% more in their next funding round.
INSIGHT 04 — Late-Stage Companies Hit HardestSeries D+ companies accounted for 61% of all layoffs by headcount — early-stage startups were lean enough to avoid mass cuts.
INSIGHT 05 — India Tech ResilienceIndian tech hubs absorbed 40% of roles cut from US-based teams via offshoring — net employment in India's tech sector grew 8% during the same period.
Dashboard Preview
2020
2021
2022
2023
2024
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Recommendations & Expected Impact
1
Workforce Scenario PlanningCompanies should model 3 headcount scenarios (base, stress, growth) annually — layoff spikes consistently follow 12-month overhiring cycles.
2
AI Upskilling Before CutsFirms that reskilled 20%+ of affected staff into AI/ML roles saw 55% lower voluntary attrition in the following year.
3
India Market PrioritisationProduct teams should accelerate India-first hiring pipelines — talent density and cost efficiency make it the most resilient market for scaling technical functions.
400K+
Jobs Analyzed
3,200
Layoff Events
50+
Countries
5yr
Time Span
Q1 23
Peak Identified
42% 29% 23%
Case Study 03 · HR Analytics & Predictive Modeling
IBM Employee Attrition Strategy
A predictive analytics project using logistic regression to identify attrition drivers and design actionable HR strategies to improve employee retention.
Python Google Colab Logistic Regression EDA Tableau
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Problem Statement & Stakeholder Context

Employee attrition leads to significant financial and operational loss. This project aims to help HR teams identify key drivers of attrition and design strategies to improve retention and employee satisfaction.

Dataset Source & Scope
  • IBM HR Analytics dataset
  • Features: salary, job role, satisfaction, work-life balance
  • Employee-level structured data
  • Binary target variable: attrition (Yes/No)
Cleaning & Transformation Summary
  • Handled missing and inconsistent HR records
  • Encoded categorical variables
  • Scaled numerical features for model performance
  • Balanced dataset for improved prediction accuracy
KPI Framework
AR
Attrition Rate
PS
Prediction Score
ES
Employee Satisfaction
WB
Work-Life Balance
Key Insights
INSIGHT 01 — Low Satisfaction Drives Attrition Employees with low satisfaction scores had significantly higher attrition probability.
INSIGHT 02 — Work-Life Imbalance Poor work-life balance emerged as a strong predictor of attrition.
INSIGHT 03 — Salary Isn’t Everything Compensation had less impact compared to job satisfaction and environment.
INSIGHT 04 — Role-Specific Risk Certain job roles showed consistently higher attrition trends.
INSIGHT 05 — Predictive Power Logistic regression effectively identified high-risk employees.
Recommendations & Expected Impact
1
Employee Engagement Programs Improve satisfaction through feedback-driven initiatives.
2
Flexible Work Policies Enhance work-life balance to reduce burnout.
3
Predictive HR Monitoring Use ML models to proactively identify at-risk employees.
3.41%
Attrition Reduction
LR
Model Used
HR
Domain
2020 2021 2022 2023 2024 Peak
Case Study 04 · Pricing Strategy & Revenue Optimization
Uber Pricing Optimization in NYC
A regression-driven pricing analysis to decode demand elasticity, evaluate fare strategies, and design a model that maximizes revenue without compromising ride volume.
Python Google Colab Pandas Linear Regression Tableau
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Problem Statement & Stakeholder Context

Ride pricing directly impacts both customer demand and platform revenue. This study was conducted to help pricing teams understand how fare components (distance, time, surge, discounts) influence booking behavior and revenue generation in a high-density urban market like New York City.

Dataset Source & Scope
  • NYC Uber ride dataset (trip-level records)
  • Features: distance, duration, fare, surge multiplier, time-of-day
  • Time coverage: multi-month ride data
  • High-volume dataset representing real urban demand patterns
Cleaning & Transformation Summary
  • Removed null and inconsistent fare records
  • Handled outliers in trip distance and fare values
  • Created derived variables: revenue_per_km, peak_hour_flag
  • Normalized time-based demand patterns
  • Encoded categorical time segments for modeling
KPI Framework
RPM
Revenue per Ride
DR
Demand Rate
FE
Fare Elasticity
SR
Surge Impact Ratio
Key Insights
INSIGHT 01 — Price Elasticity Exists Demand showed sensitivity to fare increases beyond optimal thresholds, reducing ride volume.
INSIGHT 02 — Surge Timing Matters Surge pricing during peak hours improved revenue, but excessive surge reduced booking conversion.
INSIGHT 03 — Distance Dominates Pricing Trip distance was the most influential variable in determining fare outcomes in regression analysis.
INSIGHT 04 — Discount Trade-off Discounted fares increased bookings but reduced overall revenue efficiency.
INSIGHT 05 — Balanced Pricing Wins A hybrid pricing strategy delivered optimal balance between ride volume and revenue.
Recommendations & Expected Impact
1
Adaptive Surge Pricing Implement controlled surge bands instead of aggressive multipliers.
2
Smart Discount Allocation Apply discounts only in low-demand windows to stimulate rides.
3
Regression-Based Pricing Model Use predictive modeling for dynamic pricing decisions.
11.5%
Revenue Increase
LR
Model Used
NYC
Market Focus
Additional Projects
  • Fast Food Marketing Campaign A/B Testing
  • IBM Employee Attrition: Prediction and Strategy (2023)
  • Uber Pricing Optimization in New York City
Technical Proficiency
My Toolkit
A cross-functional stack spanning data engineering, analytics, visualisation, and product management.
Data & Engineering
Python (Pandas, NumPy, Seaborn)
SQL (PostgreSQL, MySQL)
ETL Pipeline Design
Data Cleaning & Wrangling
Statistical Analysis & A/B Testing
Analytics & BI
Tableau (Dashboard Design)
Power BI (DAX, Data Models)
Excel (Pivot Tables, VBA)
Google Analytics / GA4
KPI Framework Design
Product Management
Product Roadmapping
Stakeholder Interviews & PRDs
User Story Mapping
OKR / Metric Definition
Agile / Scrum Workflows
Tools & Platforms
Jupyter Notebook / VS Code
Git & GitHub
Notion / Confluence
Jira / Linear
Figma (Wireframing)
Analytical Methods
Cohort Analysis
Time-Series Forecasting
Regression & Clustering
Funnel & Conversion Analysis
RFM Segmentation
Soft & Domain Skills
Data Storytelling
Executive Presentations
Cross-functional Collaboration
Startup & VC Ecosystem
Tech Industry Domain Knowledge
Let's Connect
Ready to turn data into decisions?

I am actively looking for data analyst, BI analyst, and associate PM roles. If you have a dataset that needs a story, or a product that needs a north-star metric — let's talk.

rashmi0505an@email.com