Employer description & source details
Design, develop, and deploy end-to-end credit risk scorecards and predictive models across the customer lifecycle, including application scoring, behavioural scoring, collections scoring, loan pricing, loss forecasting, and cross-selling. Apply statistical and machine learning techniques, including linear and logistic regression, random forests, boosting and bagging algorithms, and dimensionality reduction. Evaluate model performance, stability, predictive accuracy, and business impact to improve credit decisions and risk management outcomes. Develop data-driven strategies to enhance underwriting, portfolio monitoring, collections effectiveness, and customer segmentation. Data Science Strategy and Business Partnership Collaborate with stakeholders across business, product, risk, and engineering teams to identify opportunities for leveraging internal and external data. Develop and maintain a data science use-case roadmap aligned with business priorities and product objectives. Act as a data strategist by identifying new datasets and analytical capabilities that can enhance existing products and enable new data-driven solutions. Translate business requirements into analytical problems, modelling approaches, and actionable recommendations. Data Preparation and Exploratory Analysis Extract, process, clean, and validate large datasets to ensure data quality, consistency, and reliability. Enhance data collection processes and establish robust data preparation and validation practices. Conduct exploratory data analysis to identify trends, relationships, patterns, and potential predictive signals. Perform feature selection, feature engineering, and data mining using appropriate statistical and machine learning methods. Develop data visualisations and dashboards to communicate f
Extracted skills: python, sql, mongodb, machine learning, data analysis