Data Scientist - Machine Learning Focus

huntington | Banking

9d 16h ago

On-site

Full Time

United States of America

Description

The AI Center of Excellence (CoE) is seeking a highly skilled Lead Data Scientist with deep expertise in modern AI technologies. The ideal candidate will apply advanced knowledge in machine learning, natural language processing, and artificial intelligence to design, develop, and deploy models that accelerate innovation and support strategic decision‑making. As part of the Enterprise Data and Analytics organization, the AI CoE drives measurable impact across the business through outcome‑focused project execution.

Key Responsibilities

Collaborate with the Product Owner to manage the end‑to‑end AI development lifecycle—requirements gathering, data sourcing, model development, proof‑of‑concept demonstrations, risk governance reviews, and production deployment across both on‑premises and AWS environments.

Demonstrate strong self‑discipline and professional ownership in managing project activities, maintaining transparent communication with leadership, seeking managerial guidance when appropriate, and proactively surfacing risks, blockers, or resource needs to ensure timely delivery.

Apply automation techniques to streamline manual processes, enhance productivity and accuracy, and develop reusable, scalable self‑service tools and assets.

Stay current with emerging machine learning, cloud, and AI technologies, driving innovation by evaluating and integrating new capabilities into existing workflows and products.

Basic Requirements

PhD or Master’s degree in Statistics, Economics, Data Science, or related field.

Minimum of 3 years related work experience in AWS cloud machine learning or a similar AI platforms, with familiarity in AWS Sagemaker, Lambda, S3, Athena, etc.

Preferred Skills:

Proficiency in one or more programming languages, including Python (preferred) or R, Spark, and other modern data science tools.

Working knowledge of Large Language Models (Llama, Claude, Titan), including experience with frameworks such as LangChain, vector databases, embeddings, and prompt engineering.

Willingness to step outside traditional modeling tasks to solve broader technical or business challenges in support of team and organizational goals.

Hands‑on experience with LLM fine‑tuning

Practical understanding of advanced Retrieval‑Augmented Generation (RAG) methods such as MMR, multi‑vector retrieval, RAG‑fusion, HYDE, self‑RAG, and retrieval evaluation frameworks.

Knowledge and practical experience with Agentic AI and agentic workflows, including task orchestration, multi‑agent collaboration, tool‑use agents, and workflow automation patterns.

Hands‑on experience with advanced AWS AI/ML services such as Bedrock, JumpStart, Textract, and related ML orchestration capabilities.

#LI-NG1

#LI-Onsite

Exempt Status: (Yes = not eligible for overtime pay) (No = eligible for overtime pay)

Yes

Workplace Type:

Office

Our Approach to Office Workplace Type

Certain positions outside our branch network may be eligible for a flexible work arrangement. We’re combining the best of both worlds: in-office and work from home. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. Remote roles will also have the opportunity to come together in our offices for moments that matter. Specific work arrangements will be provided by the hiring team.

Huntington is an Equal Opportunity Employer.

Tobacco-Free Hiring Practice: Visit Huntington's Career Web Site for more details.

Note to Agency Recruiters: Huntington Bank will not pay a fee for any placement resulting from the receipt of an unsolicited resume. All unsolicited resumes sent to any Huntington Bank colleagues, directly or indirectly, will be considered Huntington Bank property. Recruiting agencies must have a valid, written and fully executed Master Service Agreement and Statement of Work for consideration.

Required Skills

awslambdas3pythonsparklangchain

Required Languages

🇬🇧 English

Description

Key Responsibilities

Collaborate with the Product Owner to manage the end‑to‑end AI development lifecycle—requirements gathering, data sourcing, model development, proof‑of‑concept demonstrations, risk governance reviews, and production deployment across both on‑premises and AWS environments.

Demonstrate strong self‑discipline and professional ownership in managing project activities, maintaining transparent communication with leadership, seeking managerial guidance when appropriate, and proactively surfacing risks, blockers, or resource needs to ensure timely delivery.

Apply automation techniques to streamline manual processes, enhance productivity and accuracy, and develop reusable, scalable self‑service tools and assets.

Stay current with emerging machine learning, cloud, and AI technologies, driving innovation by evaluating and integrating new capabilities into existing workflows and products.

Basic Requirements

PhD or Master’s degree in Statistics, Economics, Data Science, or related field.

Minimum of 3 years related work experience in AWS cloud machine learning or a similar AI platforms, with familiarity in AWS Sagemaker, Lambda, S3, Athena, etc.

Preferred Skills:

Proficiency in one or more programming languages, including Python (preferred) or R, Spark, and other modern data science tools.

Working knowledge of Large Language Models (Llama, Claude, Titan), including experience with frameworks such as LangChain, vector databases, embeddings, and prompt engineering.

Willingness to step outside traditional modeling tasks to solve broader technical or business challenges in support of team and organizational goals.

Hands‑on experience with LLM fine‑tuning

Practical understanding of advanced Retrieval‑Augmented Generation (RAG) methods such as MMR, multi‑vector retrieval, RAG‑fusion, HYDE, self‑RAG, and retrieval evaluation frameworks.

Knowledge and practical experience with Agentic AI and agentic workflows, including task orchestration, multi‑agent collaboration, tool‑use agents, and workflow automation patterns.

Hands‑on experience with advanced AWS AI/ML services such as Bedrock, JumpStart, Textract, and related ML orchestration capabilities.

#LI-NG1

#LI-Onsite

Exempt Status: (Yes = not eligible for overtime pay) (No = eligible for overtime pay)

Workplace Type:

Our Approach to Office Workplace Type

Huntington is an Equal Opportunity Employer.

Tobacco-Free Hiring Practice: Visit Huntington's Career Web Site for more details.

Data Scientist - Machine Learning Focus

Description

Collaborate with the Product Owner to manage the end‑to‑end AI development lifecycle—requirements gathering, data sourcing, model development, proof‑of‑concept demonstrations, risk governance reviews, and production deployment across both on‑premises and AWS environments.

Demonstrate strong self‑discipline and professional ownership in managing project activities, maintaining transparent communication with leadership, seeking managerial guidance when appropriate, and proactively surfacing risks, blockers, or resource needs to ensure timely delivery.

Apply automation techniques to streamline manual processes, enhance productivity and accuracy, and develop reusable, scalable self‑service tools and assets.

Stay current with emerging machine learning, cloud, and AI technologies, driving innovation by evaluating and integrating new capabilities into existing workflows and products.

PhD or Master’s degree in Statistics, Economics, Data Science, or related field.

Minimum of 3 years related work experience in AWS cloud machine learning or a similar AI platforms, with familiarity in AWS Sagemaker, Lambda, S3, Athena, etc.

Willingness to step outside traditional modeling tasks to solve broader technical or business challenges in support of team and organizational goals.

Hands‑on experience with LLM fine‑tuning

Practical understanding of advanced Retrieval‑Augmented Generation (RAG) methods such as MMR, multi‑vector retrieval, RAG‑fusion, HYDE, self‑RAG, and retrieval evaluation frameworks.

Knowledge and practical experience with Agentic AI and agentic workflows, including task orchestration, multi‑agent collaboration, tool‑use agents, and workflow automation patterns.

Hands‑on experience with advanced AWS AI/ML services such as Bedrock, JumpStart, Textract, and related ML orchestration capabilities.

Required Skills

Required Languages