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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