Lead Analytics Process Engineer

Description

Job Description Summary

The Lead Analytics Process Engineer is an important part of the team responsible for implementing GenAI and Agentic AI solutions, integrating those solutions into business processes, and monitoring those solutions for business value. This role involves working closely with senior leadership across the enterprise to enhance our GenAI and Agentic AI return on investment and to achieve business objectives. This leader should be equally comfortable with and conversant in technical GenAI subjects and process engineering.

Job Description

Summary: The Lead Analytics Process Engineer is an important part of the team responsible for implementing Generative AI (GenAI) and Agentic AI solutions, integrating those solutions into business processes, and monitoring those solutions for business value. This role involves working closely with senior leadership across the enterprise to enhance our GenAI and Agentic AI return on investment and to achieve business objectives.

This leader should be equally comfortable with and conversant in technical GenAI subjects and process engineering.

This role is pivotal in integrating GenAI solutions into business processes, mapping and automating workflows, and ensuring scalable, efficient analytics operations.

As a strategic and hands‑on leader, you will collaborate with cross‑functional teams to identify opportunities for automation, streamline data pipelines, and embed GenAI capabilities into core business functions. Your work will directly influence how data is leveraged to improve decision‑making, operational efficiency, and innovation.

This leader reports to the GenAI Implementation Director within the Enterprise Data Strategy and Analytics team.

Duties and Responsibilities:

• Identify and build requirements for GenAI and Agentic AI solutions that enhance business processes and decision making based on a deep understanding of business needs.
• Map, analyze, and optimize end‑to‑end workflows, identifying opportunities for automation and efficiency using GenAI, Agentic AI, and Robotic Process Automation.
• Partner with business stakeholders to understand current‑state processes and translate them into future‑state, AI‑enhanced workflows.
• Develop and maintain scalable process documentation, automation process maps, and performance metrics.
• Collaborate with data engineers, data scientists, and business analysts to ensure seamless integration of GenAI tools into existing systems.
• Champion the adoption of process automation technologies (e.g., RPA, workflow orchestration tools) to reduce manual effort and improve data quality.
• Monitor and evaluate the performance of implemented solutions, iterating as needed to maximize impact.
• Stay current with emerging technologies in GenAI, process engineering, and automation, and assess their applicability to business needs.
• Ensure that all Models, AI/ML Models and Tools, and User Developed Tools are appropriately documented and approved before use.
• Maintain Huntington’s strong risk culture through early identification, communication, and proactive management and mitigation of financial risks.
• Performs other duties as assigned.

Basic Qualifications:

• Master’s degree in a STEM field or Business management discipline.
• 5+ years of business experience in analytics, process mapping, management consulting, or a related field.

Preferred Qualifications:

• Proven experience implementing GenAI or AI/ML solutions in a business context.
• Familiarity with LLMs, prompt engineering, and GenAI deployment best practices.
• Some background in process mapping tools and automation platforms (e.g., UiPath, Alteryx, Apache Airflow).
• Excellent problem‑solving skills and a systems‑thinking mindset.
• Strong communication and stakeholder management skills.
• Ability to foster an inclusive and innovative working environment within the analytics team and across businesses.
• Demonstrated negotiation, influencing, partnership & presentation/communication skills.
• Ability to understand business challenges and translate them into value‑added, data‑driven solutions.
• Willingness and ability to learn new technologies on the job.
• Self‑motivated with the ability to work in a…