Srijita Chakraborty

Data-driven Product Analyst

Karnataka, Bengaluru 2+ yrs exp 88 · Excellent

About

Data-driven Product Analyst with 2 years at Condé Nast specializing in experimentation (15+ A/B tests), funnel analysis and behavioral analytics (SQL, Python, Databricks). Drove 15% subscriber lift and 30% engagement growth. Leverages AI (Databricks Genie, Claude Code) to accelerate workflows and democratize self-serve data access.

Skills & Expertise (14)

SQL Intermediate

7.4/10

Years Exp

Python Intermediate

7.2/10

Years Exp

Data Analysis Intermediate

7.0/10

Years Exp

Power BI Intermediate

7.0/10

Years Exp

Excel Intermediate

6.5/10

Years Exp

A/B testing Intermediate

6.5/10

Years Exp

Funnel Analysis Intermediate

6.5/10

Years Exp

KPI Tracking Intermediate

6.5/10

Years Exp

Tableau Intermediate

6.0/10

Years Exp

Cohort Analysis Intermediate

6.0/10

Years Exp

Databricks Intermediate

6.0/10

Years Exp

Google Analytics Intermediate

6.0/10

Years Exp

Codex Beginner

4.5/10

Claude Code Beginner

4.0/10

Work Experience

Associate Product Analyst

Condé Nast

Apr 2024 - Apr 2026

Drove a 15% increase in subscriber acquisition and a 10% lift in total revenue by using Power BI and Data Visualization to engineer comprehensive community engagement dashboards that track active vs. passive user behavior and launch a subscriber-gated community for Pitchfork. Achieved a 30% increase in user engagement and total time spent on the platform by using Python and advanced SQL (CTEs, window functions) to analyze user interaction patterns and utilize behavioral data to optimize chatbot conversational flows. Improved CTR by 12% and CVR 5% for the 'Subscribe' CTA by using A/B Testing and Funnel Analysis to optimize the subscription funnel through a multi-variant ABC test on GQ’s sticky banner, testing scroll-depth triggers and visibility logic. Delivered product recommendations and test roadmaps via 15+ hypothesis-driven A/B tests; aligned Product (success criteria, key/guardrail/secondary metrics), Design (user touch points), and Engineering (tracking, event QA) to build experiment dashboards, deep-dive analyses, and executive decks. Established a primary KPI framework utilized by executive stakeholders for strategic decision-making by using Mode Analytics and KPI Tracking to design a centralized global Time Spent (TS) and Retention dashboard that monitors cross-brand performance across apps and web. Enabled non-technical stakeholders to autonomously access real-time insights via natural language queries by using Databricks and AI Tools to migrate analytical frameworks to Databricks Genie and train AI models on complex brand-specific metrics.