Quantitative Analyst — Decision Intelligence (Equipment Finance)

About the job About Us We are an AI-driven analytics platform startup focused on building automated decision-making solutions. Our current focus is Athena, our decision intelligence product for the equipment finance industry — helping lenders turn underwriting, risk, and portfolio data into real-time, automated decisions. Role Overview This is the core quantitative role behind Athena's decision intelligence products — not a generalist business analyst or PM role. You'll own translating complex financial and risk logic into specifications our data engineering team can build against, and validate that the resulting pipelines produce numbers that are actually correct. The bar here is quantitative reasoning and data fluency, not years of BA/PM titles. There will be a structured transition period supported by our outgoing product lead. What You'll Own Translate complex business and financial logic — equipment lease valuation (net investment method, true present value at contractual rate, fair value at current market rate), risk-based pricing, portfolio risk concentration, vintage curve analysis, macro-to-industry credit risk sensitivity — into specs engineers can build against Design and evolve data pipelines iteratively: identify and specify the derived/ancillary tables needed as logic gets built out (e.g., a present-value table that has to exist before a concentration calculation can run on top of it) Validate model outputs directly against the database — pull raw and transformed data, sanity-check that logic was applied correctly, and catch anomalies before they reach production (e.g., an implausible vintage projection) Own the implementation judgment calls — null handling, edge cases, and the steady stream of clarifying questions that surface as engineers build out the logic Build and maintain real working knowledge of the schema — data type quirks, which values are stored raw vs. derived — so business logic maps cleanly onto what's actually in the tables Work directly with the founder to turn high-level business requirements into precise, unambiguous logic Must-Have Strong quantitative background — applied math, statistics, economics, engineering, actuarial science, or similar Strong SQL — comfortable writing and debugging complex queries directly against Postgres to validate outputs, not just reading dashboards built by others Real comfort with quantitative financial reasoning — time value of money, risk and probability concepts (equipment/lease finance exposure is a strong plus, not required) Ability to break an ambiguous business problem into a precise, step-by-step logical flow High agency — comfortable being the primary translation layer between founder and engineering with minimal oversight Nice-to-Have Background in credit risk, actuarial work, or quantitative/statistical modeling within lending, NBFC, or fintech Exposure to data pipeline concepts (ETL, orchestration tools like Dagster, layered bronze-to-gold architecture) FRM/CFA (partial or full) or equivalent quantitative finance coursework — not required, but signals the right reasoning muscle What We Offer Full-time role with a structured transition period, supported directly by the outgoing product lead Direct ownership of the hardest, most central problem in the product — not ticket execution Direct access to the founder and outsized influence over how the product's core logic is built A small, sharp team solving a genuinely hard problem in equipment finance Compensation ₹10–12 LPA, fixed cash, based on experience and demonstrated quantitative/data fluency. No equity component for this role.

Required Skills ['Data Analysis SQL Applied Mathematics Quantitative Finance Credit Underwriting Credit & Risk Management']

Additional Information

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