About
Senior Product Manager with 7+ years of experience across fintech, travel tech, and healthcare. Specializes in 0-to-1 platform builds, credit risk acquisition, and regulatory compliance, with a track record of driving measurable impact across U.S., Australian, and APAC markets. Currently at American Express, leading digital product initiatives targeting billions in addressable revenue.
- City: Jersey City, NJ 07311
- Email: am5506@columbia.edu
Skills
Resume
Summary
Abhishek Mani
Senior Product Manager with 7+ years of experience across fintech, travel tech, and healthcare. Specializes in 0-to-1 builds, credit risk, and regulatory compliance.
- Jersey City, NJ
- am5506@columbia.edu
Education
MS, Management Science & Engineering
Jan 2021 – May 2022
Columbia University, New York, NY
GPA: 3.79
B-Tech, Electronics & Communication Engineering
Jul 2014 – Apr 2018
Vellore Institute of Technology, Chennai, India
GPA: 3.97
Skills
Product & Strategy
0-to-1 Builds · A/B Testing · Credit Risk · Roadmapping · Regulatory Compliance
Data & Analytics
SQL · Python · Tableau · Power BI · Machine Learning
Tools
JIRA · Confluence · Figma · Adobe XD · AWS S3 · Git
AI & ML
LLM Integration · Prompt Engineering · Recommendation Systems
Certification
Certified ScrumMaster (CSM®)
Professional Experience
Senior Manager – Digital Product Management
Mar 2025 – Present
American Express, New York, NY
- Spearheading Project NOVA, digitizing the end-to-end onboarding process for U.S. Corporate Cards, building from 0 to 1 on a net-new digital infrastructure; targeting an addressable revenue of $14B
- Leading the migration of U.S. Corporate Card bank assets from TRS to AENB (Tier 4 to Tier 2), driving a projected cumulative PTI impact of $300M over 3 years
- Launched Single Repayment Loans for the Business Line of Credit, boosting small business loan applications and increasing loan origination by $20M
Manager – Digital Product Management
Jun 2022 – Feb 2025
American Express, New York, NY
- Rebuilt the Blueprint Line of Credit decision-making platform and implemented new business strategies, increasing approval rates by 50% and driving $115M PTI
- Managed end-to-end digital credit risk acquisitions across Australia, Hong Kong, and the United Kingdom, generating $12M incremental PTI
- Led bank-based underwriting expansion into the Australian market following market research, boosting approval rates by 30%
- Streamlined regulatory compliance initiatives, achieving cost savings exceeding $600K while maintaining continuous market acquisitions
Product Manager
Jul 2021 – Dec 2021
HearMe, New York, NY
- Integrated an AI-powered matching engine to intelligently pair users with listeners based on emotional needs, improving user satisfaction by 20%
- Advised CEO and COO on product strategy, roadmapping, and execution planning; managed 16 interns across user acquisition, marketing, and partnerships
Software Engineer
Jun 2018 – Nov 2020
Amadeus Labs, Bangalore, India
- Owned the full backend product lifecycle of an automation project from concept to delivery, achieving complete process automation and significantly reducing processing time
- Designed and documented internal tools through user studies conducted with cross-functional engineering teams
Projects
A few hands-on builds where I combine product strategy, decision engines, and applied AI to solve real user problems.
Personal Project · Fintech · Recommendation Engine
Credit Card Advisor
Credit Card Advisor is a recommendation engine that ranks cards based on projected annual dollar value from your actual spending, goals, fees, and usable credits rather than affiliate incentives or marketing headlines.
What it does
- Collects five quick inputs around credit range, income, spending categories, goals, and preferences in roughly two minutes.
- Evaluates 88+ cards in under a second using projected annual rewards, annual fees, and usable statement credits.
- Generates a shortlist of 3 to 5 recommended cards with reasoning, projected value, tradeoffs, and application links.
How it works
- Uses a deterministic four-factor model based on spend fit, goal alignment, fee efficiency, and eligibility, with a visible fit score per card.
- Optimizes for expected real-world value instead of generic point multipliers or sign-up-bonus hype.
- Also includes a score-lift playbook and optional read-only spend analysis to help users improve approval odds and card strategy over time.