Product Manager-III

FlexiLoans; Mumbai; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

FlexiLoans is a technology-driven digital lending platform focused on MSMEs in India. We are seeking a senior product manager to own the MSME lending stack, underwriting, risk controls, and cross-sell journeys on the LOS.

You will work across credit, fraud, and collections modules with data partners. The role requires 6–10 years of product management experience in fintech or lending, with hands-on LOS/LMS, risk/collections exposure, and strong data skills.

Qualifications

• MBA/IT degree with fintech or lending experience preferred.
• Minimum 6 years of product management experience in fintech or lending.
• Experience with LOS/LMS, risk or collections tech is highly valued.

Responsibilities

• Own end-to-end product management for MSME lending stack, underwriting, risk, and collections workflows.
• Develop and maintain rule engines, policy variants, and deployment of risk models.
• Collaborate with Data Science, engineering and business teams to ship measurable improvements.

Skills

Product management

MSME lending

Credit underwriting

LOS/LMS features

Rule engines

SQL / data analytics

Stakeholder collaboration

Education

MBA in Business Analytics, Marketing, or IT

Bachelor's in IT/CS

Tools

LOS/LMS systems

BI tools

Job description

FlexiLoans is a technology based Digital financing platform started with an endeavour to solve the problems that small businesses face in accessing Quick, Flexible and Adequate funds for growing their Businesses. Our vision is to give "Financial access at a click". Our talent pool has rockstars from diverse backgrounds - ex- Founders, investment bankers, e-commerce and payments with the passion to make a difference to the lives of 70 mn+ MSME businesses in India.

FlexiLoans.com is a pioneer in the ecosystem-based digital lending for small businesses in India. Till date, we have disbursed over 100,000+ loans worth over Rs. 5,000 Crs+ to small sized businesses across 3,200+ cities without having a single branch! We are the leaders in using technology and risk models that focus on alternate / surrogate methods for scoring customers. Our origination is 100% digital with over 100 embedded partnerships like Amazon, Flipkart, Nykaa, Paytm, Paisabazaar, META, etc. for providing credit access to MSME businesses.

Founded by CA/ISB alumni, FlexiLoans is funded by marquee funds and HNIs in the form of MAJ invest, Fasanara Capital, Sanjay Nayar (Founder - Sorin Investments, Chairman - KKR India and Ex-CEO, Citibank South Asia), Dr. Harry Banga (Founder, Caravel group), Yogesh Mahansaria (Founder, Alliance Tyres) Gunit Chaddha (Ex-CEO, Deutsche Bank, Asia Pacific), Anil Jaggia (Ex-CIO, HDFC Bank), Vikram Sud (Ex-COO, Kotak Mahindra Bank), Narayan Seshadri (Ex-Managing Partner, KPMG), Gopal Srinivasan (Chairman, TVS Capital) and Siddharth Parekh (Co-Founder, Paragon Partners) to name a few.

Our product offerings and value proposition can be accessed on our website: https://www.flexiloans.com/

A six-times certified ‘Great Place to work’ workplace, at FlexiLoans you will be working with top tier talent from diverse backgrounds hungry to make a dent in the MSME universe. We believe in people owning what you do and providing support to folks for making decisions (sometimes even wrong decisions!) all the while learning and growing with the organization. FlexiLoans is your front row seat to the MSME Fintech revolution in India!

The role in a gist:

FlexiLoans has lent to over 1,00,000 MSMEs across 300+ cities, almost all of it through one motion: a business needs capital, we underwrite, we disburse, they repay, and the relationship goes quiet. This role owns the products that decide who we lend to, how much, how fast, and how we get paid back. You will work across the full MSME lending stack, the underwriting and eligibility engine, fraud and financial-crime controls, alternate data, the collections platform, the rule engine, and cross-sell journeys on the LOS.

What we are looking for in the role:

1. Credit, underwriting and the eligibility engine:

• Productize the MSME credit underwriting workflow end-to-end, data pull, deduplication, scorecard execution, eligibility computation, deviation and approval matrix, sanction and disbursal.
• Own the eligibility engine: limit assignment, pricing and tenor logic, program-level policy variants, and multi-bureau/multi-source decisioning.
• Build credit workbench tooling that reduces manual underwriting effort per file while improving decision consistency and auditability.

2. Risk, fraud and compliance products:

• Build products around CIBIL / bureau, banking analytics, GST, KYC (CKYC, Aadhaar, PAN, video KYC), AML screening, Crimecheck and FCU workflows.
• Own the fraud control stack, de-dupe, negative lists, device and behavioural signals, document-tampering checks, FCU case management and hit-rate feedback loops.
• Own Early Warning Signals: signal design, ingestion, scoring, alert routing, and the action playbooks that follow (limit freeze, re-KYC, portfolio review, early collections trigger).
• Productise deployment and monitoring of risk models (application scorecards, behavioural scorecards, fraud and collections propensity models), feature stores, model versioning, drift monitoring, and closing the outcome-feedback loop with Data Science.
• Identify, evaluate and integrate alternate data sources for risk assessment and collections, transactional, e-commerce and marketplace, payment-gateway, telco, utility, ITR, e-invoice and other MSME footprints.
• Build the ingestion, normalisation and consent framework; measure incremental lift against cost per pull

4. Collections platform and journeys:

• Own the collections platform, allocation and strategy engine, agency and field workflows, tele-calling and digital nudges, PTP tracking, settlement and legal workflows.
• Build bucket-wise digital collections journeys (self-serve payments, part-payments, restructuring offers) that improve roll-back and reduce the cost of collections.
• Use propensity and alternate data to prioritise effort where recovery probability is highest.

5. Rule engine and loan policy:

• Own Jarvis as a product, rule authoring, versioning, simulation, champion-challenger, shadow-mode testing, rollout controls and rollback.
• Enable credit and risk teams to change policy independently, with full traceability of who changed what, when, and what it did to approval rate and loss.
• Build policy simulation and back-testing so every rule change ships with an expected impact on approval rate, ticket size and expected credit loss.

6. Cross-sell and repeat journeys on the LOS:3>

• Own the end-to-end cross-sell journey, trigger, offer, eligibility check, journey, disbursal, for existing and past customers across product lines (Term Loan, Line of Credit, Supply Chain, Partner-sourced programs).
• Build pre-approved and pre-qualified offer engines using internal repayment behaviour, bureau refresh, banking and GST data.
• Drive straight-through processing (STP) for low-risk repeat customers; reduce documentation and touchpoints without loosening control.
• Own onboarding funnel conversion and scale the journey across channels — app, web, partner APIs, DSA/RM-assisted and embedded flows.
• 6–10 years of product management experience, with at least 4 years building lending products, LOS, LMS, underwriting, risk or collections. MSME/SME lending strongly preferred
• Working fluently in the credit underwriting process for MSME lending, you can read a bureau report, a bank statement analysis and a GST return, and explain what each tells you about risk.
• Hands-on experience building LOS features for three different users: sales, credit and operations. You understand that the same journey looks completely different to each.
• Demonstrated experience with rule engines and translating written loan policy into executable, versioned, testable rules.
• Experience with at least several of: CIBIL/bureau APIs, banking analytics, GST, KYC stack, AML/sanctions screening, Crimecheck, FCU workflows, Early Warning Signals.
• Comfort working alongside Data Science on risk models, you don't need to build the model, but you must understand what it outputs, where it fails, and how to productise it responsibly.
• Strong data skills, SQL and analytics tooling; you build your own cuts before asking for them.

Qualification & Experience:

• MBA in Business Analytics, Marketing, or IT preferred. Bachelor's degree in Information Technology (IT), Computer Science, or related field
• Minimum 6 years of experience in fintech or lending background (NBFC, bank, LOS/LMS, experience with collections tech, dialers, agency management systems, payment infrastructure.)