Technical Program Manager - AI & Data Systems
New
A
Abnormal AISaaS, Artificial Intelligence
United StatesFull-TimeMiddle
Salary$110,900—$159,500 USD
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Job Details
- Experience
- 3-4 years
- Required Skills
- JiraCross-functional Team LeadershipStakeholder managementAsanaServiceNow
Requirements
- 3-4 years of program or project management experience leading complex, cross-functional technical initiatives in high-growth SaaS or enterprise tech.
- Demonstrated ability to build structure and measurable outcomes out of early-stage, ambiguous ideas.
- Proven ability to move fast, drive velocity, and manage progress through ambiguity.
- Track record of managing executive-level stakeholder relationships, including C-level communication.
- Strong experience working alongside technical team leads in AI, engineering, data, or platform contexts.
- Familiarity with AI tools and an automation instinct with application to professional workflows.
- Excellent command of project tracking and program management tools such as Linear, Jira, ServiceNow, or Asana.
- Exceptional communicator across technical and non-technical audiences.
- Demonstrated ability to drive accountability and alignment across teams without formal authority.
- Bachelor's degree in Computer Engineering, Information Technology, Business Administration, Information Security, or a related field.
Responsibilities
- Own program delivery for high-impact AI Platform and Data Systems initiatives from initiation through completion.
- Translate ambiguous mandates from IT leadership into structured, trackable programs with clear owners and success criteria.
- Serve as the coordination layer for cross-functional programs, ensuring engineering dependencies and delivery timelines remain on track.
- Maintain executive-ready status reporting for AI Data Systems leadership and C-level stakeholders.
- Identify, escalate, and mitigate risks across programs to protect delivery timelines.
- Partner with technical team-leads to align on delivery capacity, dependencies, and sequencing trade-offs.
- Utilize AI tools to create workflows and automations that improve program execution and reporting efficiency.
- Support roadmap planning and resource forecasting at the program and portfolio level.
- Drive retrospectives and continuous improvement practices to build an AI-native program management function.
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