Jun 2017 – Aug 2017 · 2 mos · Gurugram, Haryana, India

Mar 2015 – Jun 2017 · 2 yrs 3 mos

• Led the development of machine learning solutions that transformed pricing intelligence and customer experience across retail categories. Built automated pricing engines to optimize product positioning, discounting, and competitor-aware price adjustments, directly enhancing profitability and operational efficiency. Designed customer behavior models to predict returns and rejections, enabling informed merchandising and payment strategies that improved unit economics. Extended efforts to product segmentation based on discount sensitivity and implemented scalable frameworks for price experimentation. Integrated data science with business strategy to shift decision-making from reactive to predictive, embedding intelligence into core retail systems through close cross-functional collaboration.

Jan 2013 – Jan 2015 · 2 yrs · Noida Area, India

• Developed and implemented analytics and NLP-driven solutions to optimize pricing, demand forecasting, and competitive intelligence for global hospitality clients. Built statistical models to forecast destination-wise demand and historical yield pricing, enabling more accurate and strategic pricing decisions. Designed NLP systems to automate the matching of hotel room types across multiple online travel agencies, significantly reducing manual effort and scaling operational throughput. Conducted rate and competitor analyses across multiple international markets to deliver actionable pricing recommendations, and introduced ROI-focused tools that demonstrated measurable revenue uplift. Translated analytical insights into business value by driving data-backed decisions across product, pricing, and operational workflows.

Jan 2012 – Jan 2013 · 1 yr

• Designed and delivered predictive models to enhance financial collections, customer retention, and risk analytics for global clients across logistics and utilities. Developed segmentation and logistic regression frameworks to optimize collection strategies based on customer behavior and shipment patterns. Built churn prediction models using SVM to support retention planning, and validated risk and response models for major financial institutions using industry-standard metrics like PSI, Gini, and HL statistic. Acted as a bridge between technical teams and business stakeholders, delivering insights that informed strategy while mentoring new analysts on model development and validation best practices

Dec 2009 – Feb 2012 · 2 yrs 2 mos · Delhi, India

• Led business development and end-to-end client engagement while introducing data-driven capabilities into real estate advisory services. Streamlined data collection and management processes, enhancing operational efficiency. Developed a predictive model to estimate property price appreciation based on historical trends and macroeconomic indicators, positioning the firm with a forward-looking advisory offering that differentiated it in a competitive market. Combined domain knowledge with analytics to elevate strategic positioning and value delivery for clients.

Dec 2008 – Nov 2009 · 11 mos · Bengaluru, Karnataka, India

• Designed and implemented predictive models to support customer acquisition, retention, and service renewal strategies across APJ markets. Used logistic regression to score customers on purchase and churn likelihood, enabling targeted campaigns and improved resource allocation. Built prioritization models for sales and marketing outreach, supporting SMB and enterprise segments. Applied experimental design frameworks to measure campaign effectiveness and drive continuous optimization. Collaborated cross-functionally with business and marketing teams to embed analytics into core GTM operations.

Oct 2007 – Dec 2008 · 1 yr 2 mos

• Delivered end-to-end analytics solutions for financial services clients, focusing on customer loyalty, campaign effectiveness, and marketing ROI. Built regression and market mix models to identify key drivers of customer satisfaction and optimize marketing spend across channels. Validated response and profitability models across credit risk tiers, ensuring robust targeting and ROI tracking. Led segmentation and targeting efforts for both acquisition and portfolio campaigns, translating deep analytical insights into actionable strategies for client growth and retention.