class imbalance, and evaluating model performance using AUC, KS, precision/recall, etc. Understanding of model monitoring and techniques for identifying drift Experience with unsupervised learning (e.g., K-means, PCA, autoencoders) for fraud detection or segmentation Exposure to start-up or scale-up environments Familiarity with alternative data for credit scoring (e.g., device data, psychometrics) If this role looks of More ❯
class imbalance, and evaluating model performance using AUC, KS, precision/recall, etc. Understanding of model monitoring and techniques for identifying drift Experience with unsupervised learning (e.g., K-means, PCA, autoencoders) for fraud detection or segmentation Exposure to start-up or scale-up environments Familiarity with alternative data for credit scoring (e.g., device data, psychometrics) If this role looks of More ❯
clients and internal stakeholders. An eagerness to learn on the job is also required, with the company increasingly offering advanced analytical approaches to clients such as driver and factor (PCA) analysis, customer segmentation, maxdiff, conjoint etc... Prospective candidates need to be well-rounded individuals that work well within a friendly team culture and have strong analytical and communications skills. More ❯
