Operational Quantitative Analyst

Help bring clarity to operational questions and turn data into actionable insights that enhance how employees work and how customers are served. You’ll contribute to setting goals, building partnerships, and delivering analyses that support key business metrics. Join a collaborative team where your curiosity and initiative can make a real impact.

As an “Operational Quantitative Analyst – Analyst/Associate” in Data Analytics and Reporting (DART), you will help translate business questions into structured analyses and practical recommendations. You will support initiatives across call center and back-office operations, contributing to improved employee experience and efficiency through data-driven decision making. You will work with stakeholders, learn from experienced analysts, and help maintain high standards of accuracy and timeliness.

Job Responsibilities

- Collaborate with team members and stakeholders to understand business goals and success metrics.

- Assist in framing hypotheses, designing analyses, and preparing clear, actionable reports.

- Support KPI tracking, trend analysis, segmentation, and root-cause investigations.

- Manage assigned analytics tasks and contribute to multiple work streams.

- Seek feedback and guidance to develop your analytics skills and performance.

- Work with cross-functional teams to support decision-making and resource planning.

- Follow risk and control standards; help identify and report issues promptly.

Required Qualifications, Capabilities, and Skills

- Bachelor’s degree in a quantitative or related field with 4+ years or 6+ years of relevant analytics experience in operations.

- Basic understanding of project management and ability to work with guidance on analytics initiatives.

- Good written and verbal communication skills; able to summarize findings for different audiences.

- Familiarity with SQL and data analysis tools.

Preferred Qualifications, Capabilities, and Skills

- Exposure to employee experience, call center, or back-office analytics (through coursework, internships, or projects).

- Experience with reporting and data visualization tools (e.g., Tableau, Alteryx) is a plus.

- Basic knowledge of Python or other programming languages for data analysis.

- Proficiency in applied machine learning for operations (forecasting, NLP, optimization, experimentation, causal analysis).

- Willingness to learn about AWS services and modern cloud data practices.