Technical Project Manager - AI & Data Science (Hybrid)

The Johns Hopkins University; Baltimore (MD); Hybrid; USD 110,000 - 140,000; Full time

Job summary

The Johns Hopkins University’s Data Science and AI Institute (DSAI) seeks a Technical Project Manager to coordinate multiple project teams across AI/ML, data science, and software for health, science, and policy applications. You will manage scope, milestones, estimates, and status while allocating resources and driving delivery.

The role emphasizes collaboration with researchers and engineers, use of modern PM tools, and the development of scalable software practices in a hybrid work

Qualifications

• Bachelor's degree in a quantitative field required.
• Four years of software project management experience required.
• Three years of professional software development experience required.
• Education may be substituted with equivalent experience per policy.

Responsibilities

• Support multiple project teams across the institute.
• Define and document user and functional requirements, scope and milestones.
• Plan delivery, objectives, key results, reviews and status.
• Report project status to leadership and stakeholders.
• Use PM tools and processes; manage schedules and status tracking.
• Provide guidance and training in project management to researchers and staff.

Skills

Software development lifecycle

Agile methodologies

Project management

Communication and presentation

Cloud infrastructure knowledge

Collaboration with scientists

Education

Bachelor's Degree in a Quantitative Discipline

Master's Degree (preferred)

Tools

Project management software

Cloud tools

Job description

The Johns Hopkins University’s Data Science and AI Institute (DSAI) seeks a Technical Project Manager to coordinate multiple project teams across AI/ML, data science, and software for health, science, and policy applications. You will manage scope, milestones, estimates, and status while allocating resources and driving delivery.

The role emphasizes collaboration with researchers and engineers, use of modern PM tools, and the development of scalable software practices in a hybrid work