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Scientific Machine Learning Jobs in Ohio (NOW HIRING)

Skyline Scientific Inc

Software Engineer, Senior

Dayton, OH · On-site

$114K - $150K/yr

Skyline Scientific is looking for a strong Python software engineer who is excited to work on machine learning-enabled sensor-data workflows, neural network training pipelines, automation tools, and ...

Skyline Scientific Inc

Software Engineer, Senior

Dayton, OH · On-site

$114K - $150K/yr

Skyline Scientific is looking for a strong Python software engineer who is excited to work on machine learning-enabled sensor-data workflows, neural network training pipelines, automation tools, and ...

The Hartford

AI Machine Learning Engineer

Columbus, OH · Hybrid

$100K - $151K/yr

The Hartford is seeking AI Machine Learning Engineer to build Machine Learning Operations (MLOps ... Adopt and promote MLOps best practices to the Data Science community. Minimum Requirements Must be ...

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Scientific Machine Learning information

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are some common challenges faced by professionals in Scientific Machine Learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What are the key skills and qualifications needed to thrive as a Scientific Machine Learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Scientific Machine Learning jobs in Ohio? For Scientific Machine Learning jobs in Ohio, the most frequently searched job titles are:
What job categories do people searching Scientific Machine Learning jobs in Ohio look for? The top searched job categories for Scientific Machine Learning jobs in Ohio are:
What cities in Ohio are hiring for Scientific Machine Learning jobs? Cities in Ohio with the most Scientific Machine Learning job openings:
Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Ohio as of July 2026, with employment types broken down into 1% As Needed, 74% Full Time, 23% Part Time, 1% Temporary, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution.
Machine Learning Engineer with Security Clearance

Machine Learning Engineer with Security Clearance

Radiance Technologies

Beavercreek, OH • On-site

Other

Medical, Dental, Vision, Life, Retirement

Re-posted 3 days ago

Job description

Radiance is seeking a Machine Learning Engineer who will advance the artificial intelligence capabilities of the National Air and Space Intelligence Center at Wright Patterson Air Force Base. This engineer will provide expertise in data analytics and algorithm development supporting the integration and analysis of diverse data sources and develop machine learning, data mining and statistical algorithms for pattern recognition and anomaly detection. Additionally, this position will improve upon current methods for the automated processing and exploitation of large data sets. This will include R&D on projects involving the exploitation of data from sensors including investigation of state-of-the-art machine learning classification methods to detect, track, and characterize targets of interest. Radiance Technologies is an employee-owned company with benefits that are unmatched by most companies in the Dayton OH area. Employee ownership, generous 401K, full health/dental/life/vision insurance benefits, interesting assignments, educational reimbursement, competitive salaries and a pleasant work environment combine to make Radiance Technologies a great place to work and succeed. Required Experience: * A working knowledge of Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
* Experience in applying core Machine Learning methodologies: Regression, Classification, Clustering, Decision Trees, Dimensional Reduction, Neural Networks & Deep Learning, Feature Engineering Required Skills & Qualifications: * Bachelor's Degree in a quantitative field such as Physics, Engineering, Computer Science, Statistics, or a related field
* Strong programming skills in at least one of the following languages Python, Matlab, C++
* Experience with Machine Learning APIs, such as TensorFlow, PyTorch, or Keras
* Active Secret Clearance with ability to obtain and maintain a TS/SCI Desired Skills: * ML for either natural language processing, computer vision, reinforcement learning, generative modeling, or equivalent experience
* PhD in data science, mathematics, statistics, computer science, a physical science or engineering is strongly desired
* A mathematical background (Probability and Statistics)
* An experienced grasp of version control using Git for nonlinear workflows
* Thorough understanding of working in research, development and production environments
* Background in image science, imagery exploitation, spatial analysis, and computer vision are a plus
* R&D on remotely sensed data to include modeling and development of algorithms.
* Ability to work independently or in a team environment
* Strong technical writing and oral communication skills
* Active Top Secret/SCI clearance Radiance Technologies is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.

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