Senior Project Manager - Automation Initiatives (Remote)

Company Description

Vericast is the financial institution (FI) performance partner. We help banks and credit unions drive growth, improve efficiency, increase engagement and navigate change through the power of data, technology and people. Our advanced analytics, data-driven insights and integrated solution set enable better execution with agility, precision and scale. That’s why thousands of financial institutions look to Vericast and our 150 years of financial services expertise to help them achieve more. For more information, visit http://www.vericast.com or follow Vericast on LinkedIn.

Job Description

The Senior Project Manager in our Product Management Office (PMO) leads strategic, high-complexity projects and programs focused on artificial intelligence and machine learning initiatives. This role combines technical project leadership with business acumen, managing AI/ML implementations, data platform modernization, intelligent automation, and cross-functional digital transformation efforts. You'll orchestrate projects from concept through production deployment, ensuring AI solutions are delivered on time, within budget, and aligned with business value and ethical AI principles. As a trusted advisor to stakeholders and a mentor to project teams, you'll bridge technical, business, and consulting domains while championing agile methodologies and modern project management practices.

KEY DUTIES/RESPONSIBILITIES

 Automation Project Leadership & Delivery: Lead end-to-end delivery of complex automation and
machine learning projects, including model development initiatives, automation platform
implementations, intelligent automation solutions, and generative automation integrations. Drive
projects through all lifecycle phases using hybrid methodologies (Agile, Scrum, Waterfall, MLOps)
tailored to automation project needs. Manage project scope, timeline, budget, and quality
standards while navigating the unique challenges of automation projects (model performance,
data requirements, ethical considerations).Coordinate dependencies across data engineering,
automation engineering, data science, and business stakeholder teams. Navigate the automation
project lifecycle from use case identification through model training, validation, deployment, and
monitoring. (25%)

 Intelligent Automation Initiatives: Support implementation and optimization of automation
platforms, including model training infrastructure, MLOps pipelines, feature stores, and model
monitoring systems. Coordinate cross-functional teams on projects involving generative
automation applications, natural language processing, computer vision, predictive analytics, and
intelligent process automation. Partner with data science, engineering, and business teams to
deliver automation solutions that drive measurable business outcomes and ROI. Manage
relationships with automation technology vendors, cloud providers (AWS, Azure, GCP), and
automation consulting partners. Ensure responsible automation practices including bias detection,
explainability, data privacy, and governance frameworks. (20%)

 Stakeholder Communication & Collaboration: Serve as primary point of contact for automation
project teams, business owners, and executive sponsors. Deliver clear, concise communications
including status reports, executive dashboards, and risk assessments tailored for both technical
and non-technical audiences. Translate complex automation concepts and project progress into
business value language for leadership. Facilitate stakeholder alignment through sprint reviews,
model review sessions, steering committee meetings, and automation governance forums. Present
project updates, model performance metrics, and recommendations to leadership using data
visualization and storytelling techniques. (20%)

 Team Coordination & Resource Management: Coordinate distributed, cross-functional teams
including data scientists, automation engineers, data engineers, software developers, UX
designers, and business analysts. Manage daily standups, sprint planning, model review sessions,
retrospectives, and other agile ceremonies. Monitor team velocity, sprint burndown, model
development milestones, and progress against OKRs. Request and allocate specialized automation
resources based on skill requirements and project priorities. Navigate resource constrints in
competitive automation talent markets. (15%)

 Change Management & Automation Adoption: Par