Live opening · Posted 3 days ago
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About the role
Description supplied by the original job listing.
Location Name: Pune Corporate Office - Cerebrum
Job Purpose
We are seeking a detail oriented and analytical professional with atleast 2-3 years of experience in Data analytics & ML modeling.
Build and monitor ML-based propensity models for Home Loan acquisition and cross-sell, and define bureau and non-bureau risk interventions within the offer construct - enabling risk-calibrated, analytics-led targeting across a large customer franchise.
Duties And Responsibilities
PRINCIPAL ACCOUNTABILITIES
(List the accountabilities/duties associated with the job and the major activities associated with each accountability. When accountabilities are complete, try if possible to estimate the relative % of time spent on these accountabilities.)
Develop, validate and refresh ML propensity models for HL fresh acquisition, balance transfer and cross-sell using bureau, demographic, behavioral and interaction data.
Create derived and bucketed variables; run IV/WOE, correlation and stability checks to select robust, business-interpretable features.
Track lift, rank-ordering, PSI/drift and out-of-sample performance; recalibrate models on defined cycles.
Define offer-level risk rules using bureau signals (score, DPD, enquiries, utilization, vintage) and non-bureau signals (engagement, digital behavior, lead source, CRM activity).
Recommend offer amount sharpening, eligibility overlays, verification triggers and suppression logic to balance penetration with portfolio quality.
Build simple, non-overlapping, business-readable decision rules and segments that can be directly operationalized by business teams.
Write production-grade SQL and Python pipelines for data extraction, scoring and refresh; ensure data accuracy and governance.
Build monitoring dashboards and MIS on offer penetration, conversion, model performance and risk outcomes.
Partner with Business, Risk, Product, Campaigns, IT and bureau partners; present findings and recommendations clearly.
Key Decisions / Dimensions
DECISIONS
(Mention the key decisions taken by job holder at his end and the decisions for which the job holder goes to superior with options and recommendations or seek approval).|DIMENSIONS
(List the significant numerical data which will reflect the scope and scale of activities concerning this job).
Financial Dimensions
(These should be quantifiable numerical amounts like annual budgets, project costs, annual revenue, purchase value, no of transactions etc.).
Major Challenges
Technology changes
Required Qualifications And Experience
SKILLS AND KNOWLEDGE
(State the minimum acceptable proficiency for this job which best indicates the education and/or experience requirements of this job and not the incumbent).
Educational Qualifications
Qualifications
Graduate or Post-Graduate, Graduate with 2-3 years of experience, post graduate with atleast 2-3 years of experience
Experience either in financial sector or in a different sector with similar job role
Must have prior experience of business development or handling business stakeholders and partners
Must be proficient in Machine Learning, Python , SQL
Functional Skills & Behavioral Skills
Entrepreneurship skills
Stakeholder management
Agile
Persistence
Relevant and total years of Experience
Graduate or Post-Graduate, Graduate with 2-3 years of experience, post graduate with atleast 2-3 years of experience
Experience either in financial sector or in a different sector with similar job role
Work arrangement
No
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