Manager - Decision Science

2 - 5 years

10 - 20 Lacs

Posted:3 days ago| Platform: Naukri logo

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Job Description

ROLE & RESPONSIBILITIES

The successful candidate will be responsible for supporting business function teams to analyze and execute ideas and drive initiatives designed to achieve business growth along with loss mitigation goals. The core responsibilities will include.

• Orchestrate end-to-end credit-risk analytics: extract bureau + alt-data features, run feature-selection, train models, and then translate those into limit, tenor and approval rules recommendations.

• Managing Credit risk framework by creating probabilistic loss forecasts using vintage curve time series progressions, survival-hazard models and Markov chain / Vasicek ECL models.

• Identification of business opportunities by decomposing acquisition funnels, isolating the reasons for true causal lift through frameworks like T-learner, tree-based segmentation etc.

• Shape next-gen underwriting stack by crafting strategies for -- score cut-offs, tenor reshaping, limit-increase logic, use of alternate credit data points -- and quantify their P&L impact before rollout

• Simulate “what-if” scenarios and recommend cohort-specific pricing/risk shifts own the data-science storyline in CXOs and board forums securing green-lights (or kill calls) for high-stakes experiments.

Qualification : - Bachelor’s or Master’s in Engineering, Statistics, Finance, or a quantitatively focused MBA.

- 3+ years of experience in credit-risk or decision analytics domain within lending, credit cards, BNPL, or a top-tier analytics consultancy.

Technical skills : - Working experience as a Data Analyst or Business Data Analyst.

- Fluency in bureau datapoints, functional knowledge of consumer lending processes (risk/profit economics, RBI digital-lending rules, alternate credit datapoints)

- Strong command of Python and SQL for large-scale data analysis, plus hands-on experience with any BI dashboards.

- Knowledge of statistics and experience of using statistical models for analyzing datasets.

- Able to design experiments, interpret results, and prepare recommendations for C-suite, credit risk teams.

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