Lead Data Analyst: Strategic Analytics & Insights

Epsilon; Westminster (CO); On-site; USD 120,000 - 180,000; Full time

Job summary

Epsilon is seeking a Lead Statistician in Westminster, Colorado to drive accurate and timely analytic work for clients and revenue initiatives. You will apply advanced statistical methods, develop analytic code in Python and PySpark, and deliver well-documented outputs for multiple workstreams.

You will lead projects end-to-end, mentor peers, present findings to clients, and collaborate with cross-functional teams to ensure high-quality modeling, data governance, and responsible AI use across

Qualifications

• Advanced knowledge of statistical methods and modeling.
• Experience with large transactional databases.
• Experience communicating with external clients.
• Proven ability to lead analytic projects end-to-end.
• Experience with AI-enabled tools to improve workflows.

Responsibilities

• Deliver statistical deliverables on projects on time.
• Lead analytic and research projects from objectives to reporting.
• Build and validate models using standard tools.
• Document analyses and prepare client-ready materials.
• Present findings to clients and Sales; advise on implications.
• Collaborate cross-functionally to troubleshoot data issues.
• Mentor peers and contribute to team standards.
• Ensure compliance with privacy and governance.

Skills

Statistical analysis

Project leadership

Client interaction

Communication skills

Education

Bachelor's degree in statistics/quantitative field

Tools

Python

PySpark

SQL

Excel

PowerPoint

Job description

Epsilon is seeking a Lead Statistician in Westminster, Colorado to drive accurate and timely analytic work for clients and revenue initiatives. You will apply advanced statistical methods, develop analytic code in Python and PySpark, and deliver well-documented outputs for multiple workstreams.

You will lead projects end-to-end, mentor peers, present findings to clients, and collaborate with cross-functional teams to ensure high-quality modeling, data governance, and responsible AI use across