Bachelor's degree in Mathematics, Computer Science, Statistics, Economics, Finance, Actuarial Science, Science, Engineering, or quantitative field; OR 4 years of relevant education and/or experience.
2 years of experience in predictive analytics or data analysis OR Advanced Degree (e.g., Master's, PhD) in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline.
Experience in training and validating statistical, physical, machine learning, and other advanced analytics models.
Experience in one or more dynamic scripted language (such as Python, R, etc.) for performing statistical analyses and/or building and scoring AI/ML models.
Experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc.
Experience in working with structured, semi-structured, and unstructured data files such as delimited numeric data files, JSON/XML files, and/or text documents, images, etc.
Familiarity with performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
Experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc.
Experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc.