Open to opportunities

Parul Goyal

@parulgoyal

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Product & Business Analyst driving growth through experimentation, analytics, and marketplace optimization.

India

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What I'm looking for

I seek a data-driven product or marketplace role where I can lead experimentation, improve metrics through analytics, and scale forecasting and automation in a collaborative growth-focused team.

I am a Product & Business Analyst with 3+ years of experience driving growth, experimentation, and marketplace optimization at high-growth marketplaces. I specialize in KPI frameworks, experiment design, forecasting, and translating data into product decisions that improve availability, retention, and monetization.

At Zepto I led SKU-level initiatives that reduced stockouts 18% and unlocked 6–10% revenue uplift, built forecasting and alerting frameworks with 93% accuracy, and improved retention via cohort-based campaigns. At Uber I owned launch scorecards, reliability root-cause analysis, merchant ranking frameworks, and automated SQL/Python pipelines to scale analytics and save analyst hours.

Experience

Work history, roles, and key accomplishments

Current

Senior Business Analyst

Current

Zepto

Mar 2025 - Present (11 months)

Led SKU-level analytics and dashboarding to reduce stockouts by 18% and unlock 6–10% revenue uplift; designed SKU bundling A/B tests and forecasting frameworks achieving 93% accuracy to improve inventory planning and monetization.

A B Testing Forecasting SQL Superset Mixpanel Cohort Analysis Dashboarding

Data Analyst III

Uber

Jul 2023 - Dec 2024 (1 year 5 months)

Built an 11-metric launch scorecard and merchant ranking framework for APAC UberEATS, drove a 130 bps improvement in Unfulfillment Rate, and automated SQL/Python pipelines saving ~30 analyst hours per dashboard monthly.

SQL Python Experimentation Dashboarding Root Cause Analysis Merchant Ranking Data Pipelines Tableau

Business Analyst Intern

Uber

Jan 2023 - Jun 2023 (5 months)

Conducted EDA on order and rider datasets and developed rider-driven cancellation prediction models using Random Forest and XGBoost, achieving 0.95 accuracy and mitigating class imbalance.

Python scikit learn Random Forest XGBoost EDA SQL