Jayant Mathur
Data Scientist @JPMC|| Fintech || Ex-MMC ||IITG21
- Role
- Data Scientist at JPMorganChase
- Location
- Mumbai, DL, IN
- LinkedIn followers
- 500 followers
About Jayant Mathur
Data scientist with 4.5+ years of experience building end-to-end analytics & GenAI solutions that accelerate decision-making, reduce operational effort, and unlock revenue opportunities. Designed LLM-based tools, predictive risk systems, and customer analytics models that delivered measurable impact across sales, marketing, and consumer banking. Expert in translating business problems into scalable data products, supporting high-visibility initiatives such as Investor Day KPIs and deepening strategy. Known for simplifying complex analytics for senior stakeholders and influencing strategic planning. Proficient in Python, SQL, ML, NLP, Generative AI, and cloud technologies
Experience
Data Scientist
Dec 2024 — Present · Mumbai, IN
Digital Engagement AnalysisConducted deep-dive analytics on digital engagement across Starter, Youth, and Affluent segments using Adobe Analytics & internal data sources.Identified friction points in mobile/web journeys and recommended optimisations that lifted digital tool adoption by 10%.Partnered with Product, Marketing, and UX teams to drive personalised nudges and channel-level interventions, improving click-through and activation rates.Built automated dashboards to monitor digital adoption KPIs, reducing manual reporting effort by 30%Co-Branded Card AnalysisCreated data-driven deepening strategies for co-branded cardholders to increase cross-LOB product adoption. Segmented customers using behavioural & P&L-based clustering to identify high-opportunity groups for Cards & Consumer Banking.Delivered insights enabling pricing, product, and offer teams to refine P&L optimisation strategies across channels. Next Product AnalysisAnalysed customer behaviour and lifetime product pathways to identify high-propensity next-product opportunities. Enabled targeted marketing execution that resulted in a 12% rise in deepening, especially for deposits, lending, and card products.Cost of Acquisition & Deepening (CB & Cards)Quantified acquisition costs across paid, organic, and branch channels for both first and subsequent products. Identified the most cost-efficient channels for “next product” acquisition, helping optimise the channel-mix and reduce CAC by 28%.
Education
Indian Institute of Technology, Guwahati
Master of Technology - MTech, Chemical Engineering
2019 — 2021
University School of Chemical Technology
Bachelor of Technology, Chemical Engineering
2015 — 2019
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