Data Project Manager Mid-Level
Listed on 2026-08-09
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IT/Tech
Data Analyst, AI Business & Operations, Business Systems & Technology Analysis, IT Project Manager
Project Manager
We are seeking a proactive and results-driven Project Manager to support the Enterprise Risk & Compliance Data Team's Advanced Analytics function. This role serves as the primary liaison between business stakeholders, data scientists, analytics engineers, IT partners, and development teams, ensuring data, analytics, and AI/ML initiatives are prioritized, governed, and delivered effectively.
The ideal candidate combines strong project management, data analytics, process and stakeholder engagement skills with an understanding of modern data platforms, AI/ML capabilities, and Agile delivery practices. They will play a critical role in translating business needs into actionable analytics solutions that drive operational efficiency, risk reduction, and business value.
Project Leadership
- Lead end-to-end delivery of AI, machine learning, predictive analytics, and data product initiatives.
- Lead Project ceremonies including backlog refinement, planning, stakeholder reviews, and prioritization sessions.
- Collaborate with Data Scientists, Engineers, & IT Resources to operationalize machine learning models and AI-driven capabilities for Risk and Compliance.
- Identify risks, dependencies, and resource constraints while driving timely resolution.
- Provide leadership-level status reporting on project progress, budget, risks, and outcomes.
- Partner with business teams to gather requirements and define priorities.
- Translate business needs into actionable data tasks.
- Facilitate regular check-ins to align on progress and expectations.
- Translate complex technical concepts into clear business language for senior leadership.
- Facilitate workshops and requirements sessions to define business objectives and solution approaches.
- Drive user adoption through communication, training, and change management activities.
Advanced Analytics & AI Enablement
- Support model validation, monitoring, explainability, and governance requirements.
- Identify opportunities to automate manual processes through AI, machine learning, and advanced analytics.
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