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Synthetic Data Jobs in Ohio (NOW HIRING)

Socket.dev

Principal Research Scientist

Beavercreek, OH · On-site

$110 - $150/hr

... synthetic data AI/ML training techniques for multiple sensor modalities. We are looking for a qualified candidate to bring technical excellence, leadership, entrepreneurship, and creativity to our ...

Zuehlke Engineering Vietnam LLC.

$90 - $120/hr

You bring 5+ years of experience in Data & AI, with broad literacy across current topics such as Responsible AI, Agentic AI, Data Mesh, and synthetic data.* You have hands-on experience across the ...

Socket.dev

Machine Learning Engineer (Materials)

Dayton, OH · On-site

$90 - $135/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... synthetic data generation, and first-principles modeling and simulation. They will collaborate with other engineers, subject matter experts, and program managers to achieve deployment of the ...

Quantum-Systems GmbH

$162 - $243/hr

  • Life

  • PTO

Proactively enable engineering teams by identifying and securing necessary hardware, sensors, compute platforms, synthetic data environments, and real‑world testing infrastructure before ...

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Synthetic Data information

What are the key skills and qualifications needed to thrive as a synthetic data engineer, and why are they important?

To thrive as a Synthetic Data Engineer, you need a strong background in computer science, statistics, and data modeling, usually with a degree in a related field. Experience with programming languages like Python or R, familiarity with machine learning frameworks, and knowledge of data privacy tools are essential. Strong analytical thinking, attention to detail, and effective communication help in designing robust data solutions and collaborating with stakeholders. These skills ensure the creation of high-quality synthetic datasets that support research, model training, and compliance with data privacy regulations.

What is the difference between Synthetic Data vs Data Analyst?

AspectSynthetic DataData Analyst
CredentialsNone required, but knowledge of data generation tools helpfulBachelor's degree in data science, statistics, or related field
Work EnvironmentData labs, software development teams, AI/ML projectsBusiness environments, analytics teams, reporting platforms
Industry UsageAI training, testing, privacy complianceData interpretation, reporting, decision support

While Synthetic Data involves creating artificial datasets for testing and training AI models, Data Analysts focus on interpreting real-world data to generate insights. Both roles require data literacy, but Synthetic Data specialists focus on data generation techniques, whereas Data Analysts analyze existing data to inform business decisions.

What are the main challenges faced by professionals working with synthetic data in a production environment?

One of the primary challenges in a synthetic data role is ensuring that the generated datasets accurately reflect real-world scenarios while maintaining privacy and compliance standards. Professionals often need to balance data utility with the risk of introducing bias or unrealistic patterns. Collaboration with data scientists, engineers, and domain experts is essential to validate results and integrate synthetic data into machine learning pipelines. Additionally, staying updated on evolving tools and best practices is crucial for maintaining data quality and relevance.

What is synthetic data and how is it used?

Synthetic data refers to artificially generated information that mimics real-world data but does not contain any actual personal or sensitive details. It is commonly used to train machine learning models, test software, and protect privacy when sharing datasets. By using synthetic data, organizations can avoid data privacy concerns and still gain valuable insights or test algorithms effectively. This approach is especially valuable in industries like healthcare and finance where real data may be restricted. Synthetic data can be generated using various statistical techniques, simulations, or machine learning models.

What are popular job titles related to Synthetic Data jobs in Ohio?

For Synthetic Data jobs in Ohio, the most frequently searched job titles are:

What job categories do people searching Synthetic Data jobs in Ohio look for?

The top searched job categories for Synthetic Data jobs in Ohio are:

What cities in Ohio are hiring for Synthetic Data jobs?

Cities in Ohio with the most Synthetic Data job openings:

Synthetic Data jobs near you
Infographic showing various Synthetic Data job openings in Ohio as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 9% Part Time, and 6% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution.

Senior Application Architect - Test Data Management

Huntington National Bank

Columbus, OH • On-site

$65 - $87/hr

Full-time

Re-posted 11 hours ago

Huntington National Bank rating

8.1

Company rating: 8.1 out of 10

Based on 170 frontline employees who took The Breakroom Quiz

65th of 171 rated banks

Job description

Description
Job Summary:
We are seeking an experienced Test Data Management Architect to design and implement enterprise TDM capabilities supporting unit, system, integration, and end-to-end testing. This role will define the architecture, technology, processes, and provisioning model for scalable, reusable test data solutions across delivery teams. The ideal candidate has strong expertise in application architecture, data management, QA frameworks, and financial systems, with experience integrating enterprise solutions, meeting regulatory requirements, and reducing delivery friction in a fast-paced environment.
Key Responsibilities:
  • Define TDM architecture, standards, and patterns.
  • Design scalable solutions for synthetic data and test data pipelines.
  • Provide architectural and technical guidance to engineering teams and serve as a key advocate for developer adoption of enterprise Test Data Management (TDM) solutions.
  • Collaborate with Chief Data Architecture Office in support of data governance, stewardship, and metadata teams to drive appropriate controls balanced with software development utilization of test data.
  • Ensure consistency across teams and environments
  • Align architecture with enterprise data and AI strategies
  • Define and govern test data lifecycle (create, deliver, refresh, retire)
  • Design test data provisioning models and ensure consistent lifecycle and provisioning patterns across teams
  • Define approach for reusable data patterns and referential integrity

Basic Qualifications:
  • Bachelor's Degree
  • 7+ years of total related experience

Additional Key Qualifications:
  • Experience:
    • 5+ years of strong technical design and solution leadership experience, with 3+ years of application architecture experience preferred.
    • Proven experience designing and implementing data-intensive applications in financial services or risk management domains.
    • Broad range of experience in QA frameworks, basic test methodology, test automation, test driven development, and technology project lifecycles.
    • Proven experience with test data compliance in banking or financial services and methods to safeguard data (e.g., synthetic data, masking, tokenization)
    • 5+ years Agile Scrum experience
  • Technical Skills:
    • Expertise in software architecture principles, including microservices and event-driven architectures.
    • Strong knowledge of database technologies (e.g., SQL, Oracle, DB2) and data modeling techniques.
    • Understanding of CI/CD pipelines and DevOps practices.
    • Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
    • Familiarity with API design (REST, GraphQL) and integration tools (e.g., Kafka, MuleSoft, or similar).
  • Domain Knowledge:
    • Knowledge of financial services, banking regulations (e.g., KYC, AML, SOX), and global privacy laws (e.g., GDPR, CCPA).
    • Familiarity with data governance, data quality frameworks, and data lakes.
    • Familiarity with AI/ML for generating test and synthetic data.
  • Soft Skills:
    • Excellent problem-solving and analytical skills with a focus on delivering business value.
    • Strong communication skills to articulate complex technical concepts to non-technical stakeholders.
    • Proven ability to lead cross-functional teams and manage multiple priorities in a dynamic environment.
    • Ability to build strong partnerships and to work collaboratively with all business and IT areas.

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.

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