Data Science II-SUPPORT SERVICES-Data & Analytics - In House Engineering

Conduct ad-hoc analysis and scenario modeling to evaluate the impact of potential business changes or initiatives.

Support the optimization of portfolio by identifying opportunities for product enhancements, risk mitigation, and customer segmentation.

Work closely with risk, marketing, finance, and operations teams to align on data, models, and strategies.

Ensure accuracy, integrity, and consistency of data used for analytics and reporting.

Collaborate with IT and data management teams to improve data infrastructure and data governance practices.

Automate processes to ensure scalability of solutions by collaborating with cross-functional teams.

Bachelors/Masters in a quantitative field (Engineering, Statistics, Econometrics, Mathematics, Operations Research, Computer Science etc).

3-5- years of experience in Data Science

Strong programming background (Python, Pyspark, SAS, SQL etc.) with proficiency in Microsoft Office suite. Experience in financial modeling is an added advantage.

Proven track record of working with large datasets. Experience in data visualization tools such as Power BI is an added advantage.

Understanding of credit card product structures, risk models, and portfolio management techniques.

Strong quantitative skills with the ability to analyze complex datasets, derive insights, and present findings to non-technical stakeholders.

Ability to develop and deliver actionable recommendations to drive business improvements.

Excellent communication skills with the ability to convey complex data insights in a clear and concise manner.

Strong interpersonal skills and the ability to work effectively in a cross-functional, team-oriented environment.

Familiarity with ML models - XGBoost, Light GBM, Random Forests, SVMs, multivariate analysis, clustering, dimensionality reduction is an added advantage.

Experience in carrying out data processing including statistical analysis, variable selection, and dimensionality reduction, custom feature engineering, as well as the evaluation of new alternate data sources.

Experience in partnering with business teams to implement strategies in production.