Data scientist - fraud analysis

- Bachelors degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or a similar quantitative discipline; - Alternatively, 4 years of relevant experience in statistics, mathematics, quantitative analytics, or a related field may substitute for the degree. - A minimum of 2 years of experience in predictive analytics or data analysis, or an advanced degree (e.g., Masters, PhD) in a quantitative field. - Experience with training and validating advanced statistical, physical, and machine learning models. - Proficiency in at least one scripting language (e.g., Python, R) for statistical analysis and model development. - Ability to write clear and well-documented code that explains logic effectively. - Experience in querying and preprocessing data from both structured and unstructured databases using query languages such as SQL or NoSQL. - Familiarity with handling various data formats, including delimited files, JSON/XML, and other document types. - Understanding of classical supervised modeling techniques, including linear/logistic regression and decision trees. - Knowledge of unsupervised modeling methods such as clustering algorithms. - Strong communication skills to present analytical findings to non-technical stakeholders.