Lead AI Scientist - Onsite in Columbus, ML to Production

DataJobs; Columbus (OH); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Health insurance coverage

Wellness program

Life and disability insurance

Retirement savings plan

Paid leave programs

Paid holidays

Paid time off (PTO)

Job summary

Huntington Bank is building AI capabilities through its AI Center of Excellence, leading end-to-end AI lifecycle work in an onsite Columbus, OH role. You will partner with Product to deliver scalable, automated AI/ML solutions across on-premises and AWS.

Responsibilities include lifecycle drive, risk governance, and production deployments, with a focus on automation, scalability, and staying current with ML, cloud, and AI technologies.

Qualifications

• Master’s degree in computer science, statistics, economics, or related field.
• 5+ years applying statistics and machine learning to solve business problems.
• Experience with statistical analysis using advanced software; scripting languages knowledge.
• Experience with big data tools and techniques: building/deploying predictive models, web scraping, scalable pipelines.

Responsibilities

• Lead end-to-end AI lifecycle from requirements to production deployment.
• Collaborate with Product Owner to drive full AI development lifecycle.
• Deliver outcomes across on-premises and AWS environments; ensure deployment readiness.
• Develop reusable self-service AI/ML tools and assets; automate where possible.
• Stay current with ML, cloud, and AI technologies and integrate into workflows.

Skills

Statistics

Machine learning

Data analysis

Scripting languages

Education

Master’s degree

Tools

Python

R

Spark

AWS

Llama

Claude

LangChain

Vector databases

Embeddings

Job description

Huntington Bank is building AI capabilities through its AI Center of Excellence, leading end-to-end AI lifecycle work in an onsite Columbus, OH role. You will partner with Product to deliver scalable, automated AI/ML solutions across on-premises and AWS.

Responsibilities include lifecycle drive, risk governance, and production deployments, with a focus on automation, scalability, and staying current with ML, cloud, and AI technologies.