Fraud Detection Data Scientist

- Bachelor’s degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, or another related quantitative discipline; OR 4 years of relevant experience in statistics, mathematics, quantitative analytics, or a related field may be substituted in place of a degree. - Minimum of 2 years of experience in predictive analytics or data analysis; OR an Advanced Degree (e.g., Masters, PhD) in a relevant quantitative field. - Proficiency in training and validating statistical, machine learning, and other advanced analytical models. - Experience with at least one scripting language (e.g., Python, R) for conducting statistical analyses and building AI/ML models. - Capability to write clear, well-structured, and adequately commented code for technical transparency. - Competence in extracting and preprocessing data from structured and unstructured databases using query languages such as SQL, HQL, NoSQL. - Familiarity with various data formats, including delimited numeric data files, JSON/XML files, and text or image documents. - Basic understanding of conducting ad-hoc analytics using descriptive, diagnostic, and inferential statistics. - Knowledge of supervised modeling techniques for predictions, such as linear/logistic regression and decision trees. - Familiarity with unsupervised modeling methods such as k-means clustering and hierarchical clustering. - Ability to effectively communicate analytical results to non-technical stakeholders.