Model Development Role - Credit Risk Analytics - R/Python - BFS

About the Role :

- Lead the credit analytics team comprising data analysts and data scientists

- Leveraging statistical analysis on consumer research and data mining projects, including segmentation, clustering, factor analysis, multivariate regression, predictive modeling, ensembling etc.

- Design modules for advanced score development and analytical tools.

- Create competitive advantage for business through analytics and machine learning

- Co-own business targets for analytics driven business outcomes

- Create new age solutions through usage and implement ML solutions in big data environment

- Bring in best practices to develop statistical and/ or machine learning techniques to build models that address business needs.

- Use techniques from artificial intelligence/machine learning/deep learning to solve supervised and unsupervised learning problems.

- Collaborate with the team to improve the effectiveness of business decisions using data and machine learning/predictive modelling.

- Identify opportunities and deliver process improvements, standardization, rationalization and automations. Enhance and standardize performance analysis, reporting packages and business loss forecast processes

- Create new Data Products/new insights from Data science and establish regular communication with decision science leaders

- Partnering with clients and internal business owners (product, marketing, edit, etc.) to understand needs and develop models and products for organisation(fintech) business line.

- Good understanding of the underlying business and workings of cross functional teams for successful execution

- Design and develop analyses based on business requirement needs and challenges.

Essential Qualifications :

- 9+ years in advanced analytics, statistical modelling, model building, optimization, and machine learning algorithms.

- Machine Learning Algorithms: Crystal clear understanding, coding, implementation, error analysis, model tuning knowledge on Linear Regression, Logistic Regression, SVM, shallow Neural Networks, clustering, Decision Trees, Random forest, Boosting trees, Recommender Systems, ARIMA and Anomaly Detection. Feature selection, hyper parameters tuning, model selection and error analysis, ensemble methods.

- Strong with programming languages like Python/R and data processing using SQL or equivalent and ability to experiment with newer open source tools

- Best practice knowledge in credit risk - strong understanding of the full lifecycle from origination to debt collection.

- Well versed with ML algos, BIG data concepts and cloud implementations.

- Experience of implementing real-time decision analytics solutions.

- Proven track record of developing robust and insightful communications suitable for senior management, our regulators and the Board.

- Excellent communication skills. Very proactive. Interacts with other business leaders to both learn as well as influence.

- Partnership approach and collaborative attitude.

If interested please contact Parul Monga @ + 91 9818735968