Data Science Engineer - Deep/Machine Learning (6-8 yrs)

Job Description :

Responsibilities :

This role will work within the Model development function and will focus on model development, model calibration, implementation and monitoring of risk models across business functions, and development of challenger models, as necessary.

- This role will be expected to work hands-on to build and implement risk models, and bring in domain/quantitative best practices.

- Work hands-on in development, re-development, and calibration of risk and other models for lending portfolio.

- Data and quantitative analysis to support modelling decisions for underwriting, account management and internal rating scorecards.

- Work on the development of model methodologies, algorithms, and diagnostic tools for testing model robustness, sensitivity, and stability.

- Detailing of model techniques and interpretation of variables used in the models.

- Develop model performance metrics and a detailed model monitoring plan to ensure continued use of these behavioural models

- Bringing in industry best practices and consultative inputs to help deliver continuous value in advanced risk analytics.

Relevant experience :

- Relevant experience in Banking and Financial services, with experience in predictive modeling of regulatory and non-regulatory credit risk domain

- It is good to have Python coding experience.

- Experience in developing, validating models and risk management of credit risk models.

- Knowledge of various statistical techniques and proven skill in regulatory and non-regulatory credit risk modelling

- End-to-end development of credit risk and regulatory models including but not limited to - PD, LGD, EAD, Credit Scorecards, Behavioural scorecards etc.

- Develop statistical/mathematical and machine learning based models, rule fine tuning/optimization, testing, reviewing, and performing validation activities and prepare end to end model documentation.

- Detailed knowledge of data analysis / analytics / mining techniques

- Hands on expertise in SQL, ETL, Python,working with large data sets.

- Excellent knowledge of various statistical techniques

- Hands on experience in Machine Learning modeling techniques

Tech Skills :

- Strong expertise in Python, pySpark, SQL, ETL, Scala (preferred) and working with large datasets

- Prior experience in MLOps including building micro-service and deploying the model on AWS, GCP tech stack

- Knowledge of model monitoring tech stack (Kibana, Grafana, Prometheus)

- Prior experience in Deep Learning is helpful

- [Preferred]: Familiarity with databases and message queue (eg. Kafka), feature store (eg. Feast)