The ideal candidate's favorite words are learning, data, scale, and agility. You will leverage your strong collaboration skills and ability to extract valuable insights from highly complex data sets to ask the right questions and find the right answers.
Responsibilities
- Build predictive models for credit risk, collections, fraud, and other business needs in Financial Industry
- Manage and own the entire end-to-end lifecycle of building and validating predictive models along with their deployment and maintenance
- Engineer predictive features from existing data to build refined customer profiles. Identify external data assets to bring into the model mix
- Work backwards to conceptualize and design analytic model frameworks to solve business problems
- Strong communication skills to share your learnings, lead with the given strategy, get multiple stakeholders to buy into the vision and execution of the analytics roadmap
Qualifications
- Bachelor's degree or equivalent experience in quantitative field (Statistics, Mathematics, Computer Science, Engineering, etc.)
- At least 4 years of hands-on experience in building, evaluating, and monitoring ML collection risk or consumer credit scoring models for financial products
- At least 2 years of leadership experience
- Solid understanding of mathematics and statistics
- Sound knowledge of machine learning concepts such as Bagging, Boosting, Recommendation Engines, etc
- Expert in feature creation on a variety of data types
- Professional experience in building machine learning analytics model development
- Understanding of trade-offs between model performance and business needs
- Proven experience to formulate data science solutions to business problems
- Proven ability to communicate with business and know business needs
- Work experience and knowledge of more than one domain is a plus - Risk Analytics, Marketing Analytics, Fraud analytics etc.
