Data Science Analyst I

Departmental Preferences: 

• Basic ability to use generative AI tools (e.g., AI coding assistants, chat-based models) to enhance software development workflows, automate routine tasks, and improve overall analytic and documentation productivity while adhering to established engineering best practices.
• Ability to follow corporate generative AI policies and team guidelines, including thoroughly reviewing, validating, and understanding AI‑generated content for accuracy, bias, compliance, and appropriate use before incorporating it into code, analyses, or business deliverables

With significant supervision, responsible for conducting data discovery and data analysis. Develop moderately complex algorithms, solve complex business issues using statistical, algorithmic, mining and visualization techniques, most notably machine and algorithms. Work under the supervision of the project technical lead to develop components within the advanced analytics solution for complex business problems.

• Perform data querying, processing, and manipulation of data sets using state of the art data processing and analytics software. 
• Through data mining techniques, discover data patterns, generate insights, and derive inferences that drive decisions that will generate additional hypotheses, optimize business objectives, and help scope the advanced analytics assets.
• Support the successful design, development, and implementation of advanced analytics through data mining, machine learning, and descriptive and/or predictive model building using state of the art analytics software, e.g., Python.
• Produce statistical analyses and data visualizations using tools such as Tableau.  Communicate analytic insights to technical team lead to ensure models are well understood. 
• Take part in training (online, classroom, webinars, etc.) to increase skill set and technical capabilities to better serve the needs of the business. Share training materials and communicate knowledge gained to other Data Science staff to maximize knowledge transfer.

QUALIFICATIONS

• Bachelor’s degree in Business, Data Science, Statistics, Economics, Engineering, Computer Science, Operations Research, or related field is required. Master's or PhD in related field is preferred.
• A minimum of one (1) year of related experience is preferred (experience may include relevant school projects, experiences, and internships).
• Previous experience programing (at least one language) is required.
• Basic knowledge in mining and visualizing data is required.
• Basic knowledge of building advanced analytics assets such as machine learning algorithms, e.g., logistic regression, random forests, gradient boosting machines, etc. is required.
• Basic knowledge around the diagnostic measures to evaluate advanced analytics models is required.
• Basic foundational knowledge in mathematics and algorithm design is required.
• Basic knowledge in using various analytical software to write and implement algorithms is required.
• Basic skills in Microsoft Word, Excel, and PowerPoint are required.
• Ability to clearly communicate findings to project technical lead is required.