Job Description:-

Data generation and storage:

- Ongoing generation of data tags / variables based on implicit and explicit data, depicting customer purchase, risk and behavioral properties

- Maintain quality of the variables, and fill rates; Devise methods to improve data collection

Data strategies:

Data science:

- Create new variables. Imbibe the value of creating and testing various strategies across the spectrum to learn and implement data based solutions

Product:

- Create customer events, establish customer identity, analyse customer product usage behavior and engagement, suggest areas of improvement in customer engagement

Marketing:

- Establish customer funnels across channels, evaluate cost effectiveness of sub-channels and how to use data to shape acquisition while marrying sub-channel performance with customer NPV (risk and return)

Collections and operations:

- Create data based strategies to bring efficiency in collections and operations

Risk policy and underwriting:

- Creates value by monitoring performance of existing risk policy, testing new policy, bringing new variables and models and improving acquisition performance

- Manage approval rates across channels while managing risk performance

- Pro-active portfolio management to prevent delinquency through franchise management by line, portfolio offer management

- Manual evaluation and tracking of individual applications to incorporate learnings in risk policy

- Assessing different data vendors to understand various capabilities that can be used for customer assessment

- Support business by designing relevant challenger programs to test new segments and monitor its performance

- Model customer attrition, spend, revolve, month-end balances, in-voluntary attrition(risk) and bring together assumptions to make customer NPV based decisions

Policy implementation:

- Responsible for ensuring the policy rules automated in system are working as required

Fraud management:

- Responsible for minimizing fraud risks by constantly evaluating process gaps and improvising fraud check policy

Monitoring and reporting:

- Meticulous about reporting weekly / biweekly / monthly performance, and while adding value through deep process knowledge through insights on each metric movement

- Automates reports where high frequency reporting and monitoring is required

- Create metrics to evaluate performance of the solutions created

Preferred Qualifications

- Bachelor's degree from Tier-1 college: IIT, BITS, NIT

- 2-3 years of experience doing quantitative analysis

- 2-3 years of experience building statistical models, feature engineering, sparse data models

- Understanding of statistical analysis, experience with R, Python, SQL, SAS, SPSS, Visualization tools (Tableau/Domo/Power BI), etc.

- At least 6 months - 1 year of experience guiding/managing work of juniors

- Experience around credit risk & understanding of related data (bureau variables, etc.)

- Experience in initiating and driving projects to completion with minimal guidance

This job opening was posted long time back. It may not be active. Nor was it removed by the recruiter. Please use your discretion.