Mar 2024 – Sep 2025 · 1 yr 6 mos · Remote · Remote

• Overall Impact : 20% YoY Revenue Growth | 10% Increase in CM1
• Leading the Revenue & Analytics team with a focus on cost reduction and margin enhancements through deposit offers, leaderboards, Reactivation, bonus currency
• Designed and deployed hybrid ML + LLM models for high-roller prediction and churn, leveraging LLM-driven communication for real-time personalized offers — achieving a 15% increase in retention and 10% uplift in LTV
• Designed and implemented a cohort-based reactivation module, driving ₹2.5 Cr Net Revenue/month, a 2% increase in CM1, and an ROI of 400%.
• Led the development of a scalable node personalization framework for deposit, withdrawal, TDS, and rewards optimization with cohort- based optimization and A/B testing resulting in a 10% increase in Net Revenue and a 5% improvement in CM1.

Jul 2023 – Mar 2024 · 8 mos

• Making MPL profitable through Personalisation, Arresting Churn & Increasing Deposits and Product Funnels.
• ML Driven Player Personalisation: Utilizing pre and post-login player data to develop ML models for personalized offers, relevant lobby suggestions, preferred games, and effective cross-selling with distinct objective functions:
• Enhancing retention for new and high-value players
• Upgrading high-potential players to high-value players

Feb 2022 – Jul 2023 · 1 yr 5 mos

Jan 2020 – Jan 2022 · 2 yrs

• Search and Personalization Product:
• Personalization framework design: Analyzed user’s browse and purchase
• data to identify the pivots of personalization such as user cohorts, maturity,
• and stage in the purchase journey
• Design of experiments: Designed, implemented, and concluded multiple AB
• experiments across different discovery channels
• Experiments proposed:
• Homepage Journey based store personalization
• Autosuggest User cohort x Journey based store personalization
• Search page Journey aware attribute affinity
• Product page Journey aware layout changes

Jan 2018 – Dec 2019 · 1 yr 11 mos

• Framework for Product launches in Private Label:
• o Led and developed a standalone analytics product/framework which helped business to identify differentiated features/attributes and prices in which products can be launched in each category
• o This helped team to launch 100+ categories spread across hardlines, softlines and consumables
• under the umbrella of many PL brands like Flipkart Smartbuy, Perfect Homes, Billion and MarQ
• o Private label brand accounts to top contributor in sales in the relevant categories
• Identified the right set of Mobile/TV users, buyers to target for Marketing Campaign
• o Built a model using XG-boost algorithm to assign propensity to buy score for users
• o Model helped marketing team to target right set of customers and reduce the marketing cost to
• 1/10th
• o Automating the entire pipeline to help marketing team reducing manual effort and helping in
• efficient targeting

Jul 2016 – Dec 2017 · 1 yr 5 mos

• Working as a part of the Private label Team
• Key responsibilities include:
• Customer Identification and segmentation to understand category wise customer base
• o Classified customer based on relevant attributes like quality, affordability and frequency of transactions
• o Classification helps business to identify the products that are affordable without compromising with the quality
• o Successfully implemented K means clustering to cluster million customers
• Customer sentiment analysis through text analytics
• o Created a text mining solution to Identify customer’s pain and delight
• o Identified the top attributes preferred by customers in each category and gave actionable insights to cater to those preferences

Jul 2015 – Jun 2016 · 11 mos

• Dashboard solution to visualize and analyze customer’s returns for Flipkart
• o Created dashboard in Bigfoot( Flipkart’s own inhouse data platform) by using HIVE language
• o This enabled Business users to access data easily and track some of the important metrics for returns for the company
• o Dashboard is now being used as key tool by Flipkart leadership team for making decisions across returns in the company
• Developed and automated the Framework to Identify SKUs Operating at Higher Return Rate
• o Intrinsic return rate for Flipkart- To help categories to come up with intrinsic return rate (Return rate in which category will operate if supply chain, quality etc. are in-line)
• o Identified the listings that are operating at higher return rate than that of category intrinsic return rate
• o Tool is now being used as key tool by Returns team to de-list/take actions on the SKUs operating at higher return rate

Jan 2015 – Jan 2015 · 0 mo · Bengaluru Area, India

• RFM (Recency, Frequency, Monetary) analysis for behavior based customer segmentation.
• o Grouped customers based on their transaction history – how recently, how often and how much did they buy.
• o Calculated RFM scores for each customer
• o Helped Business to identify and target different segments with different messages, notifications or offers

May 2012 – Jun 2012 · 1 mo · Ranchi Area, India

• Submitted project on DRY SLAG GRANULATION
• o Designed a 3D model for a integrated system of Dry Slag granulation using ‘Solidworks’ software
• o Proposed an efficient Heat Recovery Model.