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Data Annotation For Ai Jobs in Ohio (NOW HIRING)

... AI/ML)L algorithms and applications. As a Data Domain Architect Lead within the Data Annotation ... training data for machine learning models * Lead efforts to identify patterns and trends in ...

Solid knowledge of data collection, preprocessing, and annotation for prompt development ... AI can achieve.

This position is ideal for candidates with strong attention to detail, excellent hand-eye ... Support data annotation and quality validation activities * Maintain accurate operational records ...

... AI solutions ... Do you enjoy collaborating across teams to ensure data is structured, governed, and usable for ...

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Data Annotation For Ai information

What is the difference between Data Annotation For Ai vs Data Labeler?

AspectData Annotation For AiData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, tech companies, AI projectsRemote or on-site, data processing companies
Industry UsageArtificial Intelligence, Machine LearningData management, content moderation
Job FocusPreparing data for AI algorithms through annotationLabeling data for various purposes, including AI

Data Annotation For Ai involves preparing datasets specifically for training AI models, focusing on detailed annotations. Data Labeler is a broader role that includes labeling data for multiple purposes, including AI but also other data management tasks. While both roles require similar skills, Data Annotation For Ai is more specialized towards AI development projects.

What is data annotation for AI?

Data annotation for AI is the process of labeling or tagging data—such as text, images, audio, or video—to make it understandable for machine learning models. Annotators add relevant information to raw data, helping AI systems learn to recognize patterns and make accurate predictions. This step is crucial for training, validating, and testing AI algorithms, especially in tasks like computer vision and natural language processing. High-quality data annotation directly impacts the effectiveness and reliability of AI applications.

What are the key skills and qualifications needed to thrive as a Data Annotation Specialist for AI, and why are they important?

To thrive as a Data Annotation Specialist for AI, you need a keen eye for detail, a solid understanding of data labeling concepts, and often a background in the relevant domain (such as language, images, or audio). Proficiency with annotation platforms, data management systems, and basic familiarity with tools like Excel or Python can be highly valuable. Strong communication, consistency, and time management skills help ensure accuracy and meet project deadlines. These abilities are crucial because high-quality, well-annotated data is foundational for training reliable and effective AI models.

What are some common challenges faced by data annotators working on AI projects, and how can they be addressed?

Data annotators for AI often encounter challenges such as maintaining consistency across large datasets, understanding ambiguous labeling instructions, and managing repetitive tasks. To address these issues, it's important to actively seek clarification on guidelines, participate in team discussions to align on labeling standards, and use annotation tools that flag inconsistencies. Regular feedback sessions with project leads also help improve accuracy and efficiency, fostering a collaborative and supportive work environment.
What job categories do people searching Data Annotation For Ai jobs in Ohio look for? The top searched job categories for Data Annotation For Ai jobs in Ohio are:
What cities in Ohio are hiring for Data Annotation For Ai jobs? Cities in Ohio with the most Data Annotation For Ai job openings:
Infographic showing various Data Annotation For Ai job openings in Ohio as of July 2026, with employment types broken down into 74% Full Time, 23% Part Time, and 3% Contract. Highlights an 71% Physical, 3% Hybrid, and 26% Remote job distribution.
Data and Analytics - Data Annotation

Data and Analytics - Data Annotation

J.P. Morgan

Columbus, OH

Full-time

Medical, Retirement

Posted 13 days ago


Job description

hackajob is collaborating with J.P. Morgan to connect them with exceptional professionals for this role.

JOB DESCRIPTION

Working at Chase means making a real difference every day for your customers, your community and
yourself. How? By putting others first, doing what's right and creating solutions that make lives better.
Build your career on our strong foundation and help shape what's nextâfor you and for us. Chase, a
leading provider of diverse financial services worldwide, is actively seeking service-center team
members to create lifelong engaged relationships with our customers by delivering superior service and
quality with every customer interaction

As a Data Domain Architect Analyst within Consumer and Community Banking, you will leverage your business expertise and knowledge of JPMorgan Chase products to collaborate effectively with Data Science, Analytics, and Engineering teams. You will be responsible for developing and enhancing machine learning solutions by gathering, curating, annotating, enriching, and validating data. Additionally, you will create taxonomies and other resources to train machine learning models, extract insights, conduct analysis, and potentially generate content.

Job Responsibilities:

  • Label and annotate call center transcripts and other datasets to support the development of machine learning models.
  • Review and validate the outputs of AI and ML models, ensuring accuracy as well as alignment with business goals and compliance standards.
  • Leverage GenAI tools to conduct deep dives into call datasets to uncover opportunities to improve customer experience.
  • Provide expert guidance to stakeholders on leveraging ML/AI discovery tools for actionable insights. 
  • Draft and refine taxonomies and classification schemas to enhance data organization and model training.
  • Identify and escalate anomalies, errors, or unexpected model behaviors, acting as a critical checkpoint in our AI workflow.
  • Work closely with data scientists, engineers, and business stakeholders to continuously improve annotation processes and model performance.

Required qualifications, capabilities, and skills:

  • Experience with banking products and/or customer service within the financial services industry
  • Bachelor's degree in business or comparable discipline; or equivalent level demonstrated in relevant work experience.
  • Excellent analytical and problem-solving skills and the ability to pay close attention to detail
  • Experience in working with and analyzing large real-world datasets.
  • Ability to interpret and apply business context to technical tasks.
  • Interest in machine learning and willingness to develop new skills in this area.

Preferred qualifications, capabilities, and skills

  • Prior experience with data annotation, conversational analysis, taxonomy development, machine learning projects, and/or quality assurance
  • Familiarity with industry annotation and labeling methods.
  • Working knowledge of information and data retrieval.

ABOUT US

Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs. 

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions.  We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process. 

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

Equal Opportunity Employer/Disability/Veterans

ABOUT THE TEAM

Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions - all while ranking first in customer satisfaction.

The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.