Data Scientist I

Role Overview

• Translate complex business challenges into practical, data-driven solutions using statistical analysis, machine learning, simulation, and optimization techniques.
• Design and deliver scalable solutions that support internal decision-making and power customer-facing applications.
• Leverage expertise in databases, cloud platforms, and programming to develop advanced analytical models.
• Collaborate with peers to enhance internal tools and expand the organization’s library of data science capabilities.
• Gathers, interprets, and manipulates structured and unstructured data to enable advanced analytical solutions for the business.
• Develop scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and business value.
• Select the appropriate modeling technique and/or technology considering data limitations, application, and business needs.
• Develops and deploys models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
• Communicate outcomes to non-technical business colleagues with focus on business action and recommendations.

Requirements

• 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 may be substituted in lieu of degree.
• 4 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 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.
• 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 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.

Tech Stack

• Cloud
• NoSQL
• Python
• SQL

Benefits

• comprehensive medical, dental and vision plans
• 401(k)
• pension
• life insurance
• parental benefits
• adoption assistance
• paid time off program with paid holidays plus 16 paid volunteer hours
• various wellness programs
• career path planning and continuing education