Saurabh Y.
Senior Analytics, Assistant Manager - Growth Strategy
Notable Highlight
Built an agentic BigQuery chatbot using Gemini and Claude that synthesizes leadership-ready root-cause analysis, currently in production at Noo. Reduced Meta retargeting spend by approximately 30% through incrementality testing.
Experience Summary
EXPERIENCE: Saurabh is a Growth Strategy and Analytics professional with 8+ years of experience across e-commerce, ad-tech, logistics, and business analytics. He is currently working as an Assistant Manager, Growth Strategy at Noo., where he leads marketing analytics, CRM analytics, traffic forecasting, customer acquisition analysis, and growth strategy initiatives. He manages a team of five analysts and partners closely with marketing, product, finance, and business teams to translate data into actionable growth decisions. His experience includes working on customer funnel analysis, GMV forecasting, marketing investment planning, paid media performance analysis, attribution models, and lifecycle campaigns. Prior to Noo., he spent over three years at GT as a Senior Data Analyst, focusing on advertising analytics, attribution accuracy, fraud detection, and machine learning-driven optimization.
Key Achievements
SIGNIFICANT ACHIEVEMENT: One of Saurabh’s key achievements was building an AI-powered agentic analytics chatbot at Noo. using BigQuery Data Agents, Gemini, Claude, and orchestration workflows to provide leadership with instant insights and executive-ready summaries from business data. He also developed a retargeting suppression framework with A/B testing and holdout experimentation that reduced Meta retargeting spend by approximately 30% while improving campaign efficiency. In addition, he created marketing spend optimization models in Python that reduced planning cycles from nearly two days to around one hour by automating channel-level investment recommendations. During his time at GT, he implemented machine learning and statistical modeling solutions that improved ad targeting efficiency, reduced click fraud, and strengthened attribution accuracy. His work also contributed to an 8% increase in CTR by optimizing ad-request bidding strategies without impacting margins.
Skills
Work Experience
Assistant Manager - Growth Strategy
Noo. · Nov 2024 – Present
- Lead a team of 5 analysts driving traffic forecasting, marketing investment planning, and growth analytics across customer acquisition and conversion performance.
- Built an agentic Chatbot on Noo.'s BigQuery Data Agents, a multi-agent orchestrator (Gemini + Claude) that dispatches specialist analysts, validates external factors (weather/holidays), and synthesizes leadership-ready root-cause writeups; in production use by leadership.
- Built a retargeting-suppression framework to filter customers who convert organically or never convert; designed an A/B + holdout incrementality test that cut Meta retargeting spend by ~30%.
- Built an upper-funnel traffic forecasting model (sessions, clicks) and a live RCA dashboard with an embedded Gemini assistant for self-serve querying, giving the team instant root-cause answers across channels and categories.
- Automated multi-channel marketing spend planning in Python, deriving historical trends and channel-level CIR to recommend reallocation, cutting planning cycle time from ~2 days to ~1 hour.
- Built a Python-based bank-offer & discount-burn forecasting tool that cut offer-construct turnaround from ~2 days to near real-time, enabling stakeholders to model burn and negotiate terms live with bank partners.
- Integrated A/B-tested, LLM-generated push-notification copy into daily execution, driving 1.75x engagement and 1.5x conversion vs generic copies.
- Prototyped a generative-AI creative engine (Vertex AI Gemini + Imagen) auto-producing campaign copy and banner visuals for customer-health cohorts.
- Developed customer & marketplace affinity targeting by combining user's multi-touchpoint app interactions, historical behavior, and marketplace preference, delivering hyper-relevant communications, resulting in +20% engagement uplift.
Senior Data Analyst - Data Science and Analytics
GTI · Mar 2021 – Nov 2024
- Increased system-wide Click-Through Rate (CTR) by 8% by optimizing the bid price for varying quality ad-requests without compromising on margin.
- Performed large-scale visitation anomaly root cause analysis (RCA) and implemented performance guardrails, improving visitation-driven sales by 16% and strengthening attribution accuracy.
- Designed and deployed fraud and anomaly detection frameworks using statistical modeling to detect invalid clicks and high-CTR bundle abuse, protecting advertising revenue and improving campaign stability.
- Built a Geo-signal intelligence layer using clustering models to identify ~40% fraudulent and ~30% low-quality visitation signals, reducing false attribution and improving location-based ad targeting reliability.
- Developed temporal mobility-based signal validation logic to filter unrealistic user movement patterns across sequential location pings, improving dwell-time estimation and visitation attribution accuracy.
Management Trainee
WTI · Oct 2019 – Mar 2021
- Streamlined logistics operations through analytics, using SQL and Python to optimize vehicle loading, transit, and proof-of-delivery processes.
- Carried out root cause analysis of various issues faced during customer on-boarding and operational execution by analyzing Voice-of-Customers from Partner care queries.
- Solved the critical issue of improper communication via automated messaging at various points, clearly laying out terms, conditions, payout, and deductions in case of poor service, resulting in 25% decrease in customer queries.
- Optimized routes for a fleet of 7 lakh vehicles using a data-driven approach with SQL and Python, improving on-time POD collection by 33% and reducing payment cycles for shippers and fleet owners by 15 days.
Assistant Manager
BAVR · Aug 2017 – Nov 2018
- Efficiently handled plant operations, resolved critical system failures, and reduced their occurrence frequency.
- Improved system availability by 12% by reducing downtime frequency, cutting flushing time from 8 hours to 3 hours of Hyper Dense Phase System.
- Reduced Aluminium fluoride consumption by 3% in pots, reducing aluminium production costs.