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.
8 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 6 years of experience in predictive analytics or data analysis.
6 years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models.
4 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.
Expert ability to write code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency).
Strong experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc.
Strong 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.
Excellent demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
Proven ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
Project management experience that demonstrates the ability to anticipate and appropriately manage project milestones, risks, and impediments. Demonstrated history of appropriately communicating potential issues that could limit project success or implementation.
Expert level experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic models, discriminant analysis, support vector machines, decision trees, forest models, etc.
Expert level experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc.
Demonstrated experience in guiding and mentoring junior technical staff in business interactions and model building.
Demonstrated ability to communicate ideas with team members and/or business leaders to convey and present very technical information to an audience that may have little or no understanding of technical concepts in data science.
A strong track record of communicating results, insights, and technical solutions to Senior Executive Management (or equivalent).
Extensive technical skills, consulting experience, and business savvy to interface with all levels and disciplines within the organization.
Relocation assistance is available for this position.