Focus on developing clear and concise analytical approach for problem solving with client partnership

Strong understanding of ML libraries and applications e.g. Neural Net, SVMs, boosting methods and implementation using R/Python. Should have academic paper level understanding of math e.g. linear algebra, calculus etc.

Ability to code on SAS/R for data munging, analysis and insights and/or languages like Python for creating efficient production ready code

Managing delivery of projects including timely communication, setting milestones and tracking

Generating actionable insights for business/KPI improvements

Ability to work independently, structure analyses and handle multiple priorities

Develop and Maintaining Credit Risk Models & Strategies

Rich experience in at least one production ready deployment of Machine Learning algorithms is a plus.

Experience in working on real life large messy dataset to solve real business problems. Kaggle experience is a plus.

Experience in Financial Services/ Fintech/ BFSI along with Credit Risk in Retail Banking and Data Science

5 years of experience in building predictive models (Application Scorecard, Behaviour Scorecard, Collection Score Card, Loan Pricing, Loss Forecasting, Cross Sell Model)

Experienced Statistical software skills (e.g. SAS, R, Python

Strong understanding of ML libraries and applications.

Strong experience in leading credit risk model development, validation, deployment, and documentation

Analytically oriented with ability to grasp complex risk management strategies, processes, models and draw insights / identify areas of opportunity

Strong track record of delivering on high value / critical projects. Ability to communicate with senior stakeholders crisply and confidently on findings, status, bottlenecks, and escalations

Proven track record of delivering in a start-up environment would be a plus

Strong written and oral communication skills