Product Manager

You will own the Ki score from India end to end. The KISS score gives lenders explainable credit decisions for people with little credit history, combining bureau, bank statement, GST, and alternate data with business rules to reach an approve or decline decision. You will decide where it goes next and how it is priced, as well as how it is built: the scoring API that lenders integrate with, the data and models behind it, and the new modules on top.

The core responsibilities for the job include the following:

Strategy, pricing, and prioritization:

• Set the KPI score roadmap and decide what gets built first, weighing revenue, client commitments, and the effect on credit outcomes.
• Own pricing and costing for Ki Score and its modules, and build the commercial proposals that go to lenders with the sales team.

Alternate data and research:

• Go after data most lenders have never used, from satellite and geospatial signals to asset depreciation curves, farm-level data, and sources no one has tried yet. Find the ones that change who gets credit and on what terms.
• Lead the research: frame the hypothesis, test the lift with our data scientists, and put what works into production.
• Build out the alternate data lake that brings these sources together.

Scoring platform and API:

• Own the universal Ki score API and each lender's configuration of it, including the admin panel.
• Build self-serve tools, starting with a sandbox where lenders can test the KIS score on their own.
• Own the business rules engine (BRE) modules that sit on top of the models.
• Set up automated backtesting of models and policy rules.

Lender deployments and models:

• Own KI score deployments for our lending partners and for Kaleidofin's own NBFC, from data requirements through go-live and production monitoring.
• Work with the data science team on new model builds, including new asset classes, and decide what goes into production.

Origination use cases:

• Own income estimation and other origination use cases built on the KISS score.
• Own Account Aggregator data at origination, including the AA SDK.
• You will also define and track how the KISS score performs in production: approval rates at controlled risk, delinquency, turnaround time, and adoption.

Requirements:

• 3 to 7 years of experience as a product manager on ML or data products, or as a data scientist. Data scientists looking to move into product management are welcome to apply.
• A working grasp of how credit or risk models are built, validated, and monitored in production.
• Commercial judgement: you can make the business case for what to build and help shape pricing.
• Comfortable with APIs and data: you can read an API spec, query data in SQL, and spot data quality problems.
• Able to turn an ambiguous problem into clear requirements that engineering and data science can build from.
• Clear communication with both technical and business audiences.

Bonus (not mandatory):

• Experience in credit underwriting, risk, or lending, ideally in India.
• Exposure to alternate data, account aggregator, or bureau data.

Skills

Data, ML, Machine Learning, Product, SQL