Analytics Project Lead

Responsibilities

• Manage the day-to-day operational and tactical aspects of company projects.
• Cook up and execute project work plans to meet changing needs and requirements.
• Identify resources needed and work with leaders to assign individual tasks.
• Prepare and review project-related documents prepared by the team before passing along to stakeholders, ensuring they are complete, current, and stored appropriately.
• Effectively apply project management methodologies and best practices.
• Perform risk analysis and recommend actions to mitigate risk.
• Ensure projects meet stakeholder objectives.

Requirements

All candidates must have the ability to work in Lansing, Michigan.

Qualifications

• Bachelor’s degree from four-year college or university, or several years related experience or training, or equivalent combination of education and experience.
• Experience leading delivery of analytics, data science, business intelligence, or machine learning initiatives.
• Familiarity with the end-to-end analytics lifecycle, including data engineering, modeling, deployment, and operationalization of insights.
• Proven ability to partner with data scientists, data engineers, and business stakeholders to deliver data-driven solutions that produce measurable business impact.
• Exhibit good levels of organization, communication, and leadership.
• Proven performance delivering high-quality solutions on individual projects.
• Ability to write routine reports and correspondence.
• Facilitate team meetings and handle project conflicts within and outside of the team.
• Understand complex concepts related to the project.
• Inspire other team members to attain goals and pursue excellence.

Benefits

• A competitive base salary.
• Matched 401(k).
• Fully-funded pension plan (once vested).
• Bonus programs.
• Generous paid time off including holidays, vacation days, personal time, and sick leave.

Pay

Competitive base salary.

Schedule

Not specified.

Skills

• Project management methodologies and best practices.
• Risk analysis and mitigation strategies.
• Data engineering, modeling, deployment, and operationalization of insights.
• Collaboration with data scientists, data engineers, and business stakeholders.
• Writing routine reports and correspondence.
• Facilitating team meetings and handling project conflicts.
• Understanding complex project concepts.
• Inspiring team members to achieve goals and pursue excellence.