Bachelor's degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline; OR 4 years of experience in statistics, mathematics, quantitative analytics, or related experience (in addition to the minimum years of experience required) may be substituted in lieu of degree.
4 years of experience in a 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 and 2 years of experience in predictive analytics or data analysis.
2 years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models.
2 years of 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 writing code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency).
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.
Experience in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
Ability to assess regulatory implications and expectations of distinct modeling efforts.
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.
Experience communicating analytical and modeling results to non-technical business partners with emphasis on business recommendations and actionable applications of results.

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