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Description supplied by the original job listing.
Own the data function for the digital lending team, spanning both conversion analytics and credit risk analytics. The mandate is to drive up conversion and approval rates while keeping credit risk firmly in the right zone. The two must be optimised together, never one at the expense of the other. This role turns the team's data into decisions across marketing, product, operations, and credit.
Responsibilities:
Conversion analytics: instrument and analyse the full funnel; identify where and why borrowers drop off, and quantify the opportunity behind each fix.
Credit risk analytics: monitor and improve approval rates and portfolio risk together, ensuring that conversion gains do not quietly degrade credit quality.
Balancing the trade-off: provide the analysis that lets the team push conversion and approval rates up while keeping risk metrics within agreed thresholds.
Decision support: give marketing, product, and operations the channel-, journey-, and cohort-level insight they need to prioritise CAC, payback, activation, approval, repeat borrowing, and risk.
Data infrastructure: build and maintain the reporting, dashboards, and data pipelines the team relies on; make the numbers trustworthy and self-serve.
Experimentation: design and read experiments rigorously so the team learns what actually moves the needle.
Governance: ensure data quality, definitions, and metric accountability are consistent and defensible across the team.
What Success Looks Like:
Conversion and approval rates improving together, with credit risk held within target.
Every strategic lever backed by a clear, agreed metric the team is accountable to.
Marketing, product, and operations making faster, evidence-led decisions.
Requirements:
Experience of 10 years in analytics
Strong, demonstrated experience leading the data function of a digital lending team.
Depth across both conversion analytics and credit risk analytics, able to drive conversion and approval rates while keeping credit risk in the right zone.
Fluency in analytics tooling, experimentation, and building trustworthy dashboards and pipelines.
Ability to translate data into decisions for non-technical stakeholders across the team.
Good-to-Have:
Experience in an NBFC, bank, or fintech lending environment.
Exposure to credit underwriting, scorecards, or risk modelling.
Experience building a data function and team from an early stage.
Experience
8-10 yrs
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