Role will be responsible for:

- Leading and managing a team of data scientist and data engineers, leading the projects in Artificial Intelligence and Data Science space

- Translating use cases to analytics agenda, including data, approach and validation strategy

- Leading multiple projects with diverse scope and complex business and technical challenges across several business units and functions

- Developing best practices for data science, considering the full analytical lifecycle

- Developing guidelines for the evaluation of new analytical tools and platforms

- Acting as an evangelist for data science in the enterprise community

- Identifying business opportunities and solutions

- Deriving and implementing best approaches for predictive and prescriptive analytics

- Acquiring or develop innovative methodologies

- Leading and guiding analytics projects across business groups

Desired Profile:

- Looking for candidates with extensive experience leading, coaching, and developing people and robust teams

- Must have knowledge of most of the following quantitative fields like Machine Learning, Advanced Statistical Analysis & Modeling, Deep Learning, Causal Inference & Program Evaluation, Reinforcement Learning, Behavioral Economics, Design of Experiments, Multi-Objective Optimization, Decision Theory, Bayesian Statistics, and Network Science etc.

- Must have strong data proficiency, exploratory data analysis, data visualization, and story telling skills

- Must have knowledge of well-designed experiments and statistical analysis concepts

- Must have knowledge of Big Data technologies such as Cloud Computing, GPU Computing, Hadoop, Spark, MapReduce or related Massively Parallel Processing technologies

Risk Analytics:

1. Development & Validation of Application Scorecards for various loan segments

2. Controlling Business Attrition using Commercial Bureau Triggers

3. Risk Monitoring of Loan Portfolio

4. Monitoring of Loan portfolio:

5. Delinquency Analysis

6. Flow Rates

7. Vintage Curve

8. Attrition Analysis

9. Exploring Multi Bureau Usage for Credit Underwriting in SME/Retail Segment

Sales / Marketing analytics

Using statistical & predictive modelling techniques like linear regression, logistic regression, decision trees, k-means clustering etc. for various areas of Asset Analytics like:

1. Customer acquisition and attrition

2. Portfolio level customer segmentation

3. Propensity for cross-holdings, product per customer, next-best product

4. Identifying potential customersfor up-migration to higher customer segment

5. Development of X-sell programs

6. Generating Analytics based Leads using Commercial Bureau data

7. Loan approval Scorecard

8. Risk based pricing model

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