Turning messy data into decisions that move metrics
3+ years owning analytics products for enterprise B2B SaaS — surfacing a 48% revenue undercount that reset leadership's pricing strategy, building bug intelligence across 8 product cohorts, and shipping dashboards consumed daily by C-suite and PMs. Hands-on with SQL, Power BI, Python, and GenAI tooling.
Enterprise B2B SaaS · India & US markets
Analytics serving 50+ business stakeholders
Internal tooling & product analytics
Designed and owned a structured bug intelligence framework across 8 product cohorts — analysing 350+ bugs for qualification rates, critical failure patterns, postmortem quality, and platform-level breakdowns. Identified the highest-risk cohort (63% NQ rate, 50% of all company-wide criticals) and the best-performing (72% meaningful postmortems). Delivered P0–P2 prioritised remediation actions per PM and Tech Lead, adopted org-wide.
Built SQL-based pipelines tracking ARPA, ARPU (per agent + per product quantity), MRR, and client count month-over-month. Corrected broken calculation logic that had been underreporting revenue for months.
Built product adoption trackers across two platform variants tracking MAU, total calls, CRM preferences, and agent-level usage. Monitored adoption trends and proactively surfaced a sharp volume decline before leadership escalation.
Built a GenAI-powered wrapper that auto-summarises bug trend data, cohort health, and feature ideas per sprint — reducing manual pre-review time for the product team and enabling faster sprint planning decisions.
Designed the product vision and information architecture for an internal decision intelligence platform aggregating customer health, product usage, operational, and revenue signals into one 360° view — built for management, PMs, sales, and customer success teams at a SaaS company.
Looking for product analyst, product ops, or AI analytics roles where data shapes real decisions. I move fast, think in systems, and leave every dataset more trustworthy than I found it.