Project Manager

Project Manager at Insight Global Project Manager at Insight Global in Weehawken, New Jersey Posted in 1 day ago.

Type:

full-time

Job Description:

Position:

Project Manager Location:

Midtown East- 3 days in office, 2 remote

Duration:

12 month contract to hire

Pay:

80-85 hr W2

Required Skills & Experience:

• 5-10 years of Project Management experience on complex, cross functional initiatives
• Experience working with

AI/ML/DS

product delivery

• Experience delivering in agile or hybrid environments and managing multiple workstreams
• Strong command of PM fundamentals (project plans, RAID, status reporting, cadence)
• Experience supporting data, analytics, or technology product initiatives
• Independent, strong communicator effective with both technical teams and senior leaders Nice to Have Skills & Experience;
• Front office experience within investment banking, capital markets, PE, VC, or hedge funds
• Exposure to AI, machine learning, or data science product delivery
• Experience managing AI-powered tools, models, or automated workflows
• Background supporting front-office or client-facing technology

Job Description:

Insight Global is seeking a Data & Analytics Project Manager to support a fast-moving AI, data science, and analytics portfolio within a global financial services environment. This individual will bring structure and clarity to high-velocity product teams, managing multiple concurrent workstreams from prototype through production. The ideal candidate thrives in iterative environments, partners closely with technical and business stakeholders, and delivers clear execution, communication, and visibility without slowing teams down. This is a highly visible role supporting innovative, data-driven solutions used across front-office and corporate functions. This is a hybrid role in Midtown, NYC requiring 3 days a week onsite.

Day-to-Day:

• Maintain a consolidated, real-time view of the Advanced Data Analytics (ADA) portfolio
• Manage lightweight but rigorous project plans across multiple AI and data science initiatives
• Track delivery from POC and pilot through production
• Run recurring team meetings and working sessions; document decisions and action items
• Maintain RAID logs and proactively surface risks, issues, and dependencies
• Escalate blockers early with clear impact assessment and solution options
• Coordinate across data science, engineering, and business stakeholders
• Produce clear, concise status reporting for senior leadership
• Support intake, prioritization, and scaling of analytics initiatives across business lines