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Scientific Machine Learning Jobs in Ohio (NOW HIRING)

Globaldev Group

$100 - $150/hr

... science capabilities. Requirements: * 5+ years of experience in machine learning / applied ML roles ... with production ownership * Proven track record of deploying and maintaining ML systems in real ...

New

Quora

$139K - $168K/yr

D. in Computer Science, Engineering or a related technical field * Strong understanding of mathematical foundations of Machine Learning algorithms * Experience of transformer models and LLM ...

Quora

$139K - $168K/yr

D. in Computer Science, Engineering or a related technical field * Strong understanding of mathematical foundations of Machine Learning algorithms * Experience of transformer models and LLM ...

Quora

$139K - $168K/yr

D. in Computer Science, Engineering or a related technical field * Strong understanding of mathematical foundations of Machine Learning algorithms * Experience of transformer models and LLM ...

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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 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 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 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 August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Senior Machine Learning Engineer Germany

Globaldev Group

On-site

$100 - $150/hr

Other

Posted yesterday

New

Job description

We are looking for a Senior Machine Learning Engineer to design, own, and scale predictive systems that power VIS.X, a programmatic advertising platform.

You will take end-to-end responsibility for high-impact ML initiatives (e.g., pricing optimization, bid prediction, performance forecasting, delivery optimization) and translate complex business problems into robust, production-grade machine learning systems.

This is a senior individual contributor role with leadership potential. You will help shape our ML architecture, standards, and long-term AI strategy, with the opportunity to grow into a team lead role as we expand our data science capabilities.

Requirements:
  • 5+ years of experience in machine learning / applied ML roles with production ownership
  • Proven track record of deploying and maintaining ML systems in real-world environments
  • Strong Python skills (e.g., pandas, scikit-learn, PyTorch/TensorFlow)
  • Solid knowledge of statistics, experimentation design, and model evaluation
  • Experience working with large-scale datasets and performance-critical systems
  • Understanding of MLOps principles (model lifecycle, monitoring, CI/CD integration, retraining pipelines)
  • Strong problem ownership mindset - ability to independently structure ambiguous challenges
  • Ability to translate business trade-offs into modeling decisions
  • Experience in AdTech, marketplaces, or auction-based systems is a plus
  • Experience working in high-scale, real-time systems is a plus
Responsibilities:
  • Take ownership of machine learning problems from concept to production
  • Design, build, and deploy predictive models (e.g. pricing, bidding, optimization, forecasting)
  • Develop scalable feature engineering and data pipelines for large-scale datasets
  • Define experimentation frameworks (A/B testing, offline validation, model comparison)
  • Ensure production-grade MLOps: monitoring, retraining, drift detection, reliability
  • Collaborate closely with DevOps, Product, Engineering teams to align ML with business impact
  • Quantify model impact on revenue, margin, and performance KPIs
  • Contribute to building our long-term ML architecture and best practices
What we offer:
  • Comfortable environment, challenging tasks and a long-term interesting project
  • Covered 20 days of vacation
  • Working with top notch equipment
  • Bookkeeping by a professional accountant
  • Help and support from our caring HR team
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