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Scientific Machine Learning Jobs in Iowa (NOW HIRING)

B2E Marketing

Marketing Data Scientist

Des Moines, IA · On-site

The Role We're looking for a Marketing Data Scientist who brings strong technical foundation while ... Apply statistical and machine learning techniques to optimize marketing outcomes. * Explore and ...

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Pwc

Payer Healthcare Data Scientist, Manager

Des Moines, IA · On-site

$99K - $232K/yr

The Opportunity As part of the Operations Consulting team, you will apply advanced data science and machine learning techniques to large-scale claims, clinical, and member data to surface actionable ...

Auzmor

Robotics & AI Research Engineer

Des Moines, IA · On-site

PhD or Master's degree in Artificial Intelligence, Machine Learning, Robotics, Computer Science, or a related field. • Experience : • 2+ years of experience in AI research applied to robotics ...

Pwc

CTIO AI Engineering Manager

Des Moines, IA · On-site

$73.50K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

RFA Engineering

AI Test Automation Engineer

Urbandale, IA · On-site

$90K - $150K/yr

Leverage machine learning, agentic AI, large language models, or intelligent agents to improve ... Bachelor's degree in Computer Engineering, Software Engineering, Computer Science, Electrical ...

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Scientific Machine Learning information

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 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 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 Iowa? For Scientific Machine Learning jobs in Iowa, the most frequently searched job titles are:
What job categories do people searching Scientific Machine Learning jobs in Iowa look for? The top searched job categories for Scientific Machine Learning jobs in Iowa are:
Scientific Machine Learning jobs near you

Marketing Data Scientist

B2E Marketing

Des Moines, IA • On-site

Full-time

Posted yesterday

Job description

About Us

B2E Data Marketing is a data-driven marketing and analytics firm focused on helping clients leverage data to drive smarter decisions, better targeting, and measurable growth. As a small, collaborative team, we value flexibility, shared ownership, and innovation.

The Role

We're looking for a Marketing Data Scientist who brings strong technical foundation while also contributing to client strategy, innovation, and product development. This role will help extend and scale the data and analytics capabilities B2E has built, while supporting our continued evolution into AI, machine learning, and predictive marketing solutions.

Note: Candidates must be authorized to work in the U.S. (no visa sponsorship). B2E also does not pay for relocation.

Key Responsibilities

  • Develop predictive models for targeting, segmentation, and campaign performance.
  • Apply statistical and machine learning techniques to optimize marketing outcomes.
  • Explore and implement emerging AI/ML capabilities to enhance product offerings.
  • Conduct A/B and multivariate testing to improvement marketing effectiveness.
  • Design and maintain data pipelines across CRM, digital platforms, and third-party data sources.
  • Ensure data quality, consistency and scalability across projects.
  • Leverage tools such as Alteryx, Python and R to automate workflows.
  • Develop dashboards and reporting to translate data into actionable insights.
  • Support audience development, data enrichment, and marketing execution across channels.
  • Collaborate on product innovation and roadmap development for new data-driven solutions.
  • Translate complex data into clear, actionable insights to clients and internal stakeholders.
  • Manage multiple projects in a fast-paced, collaborative environment.

Core Skills & Qualifications

  • Bachelor's degree in Data Analytics or a related field.
  • 25 years of experience in data science, analytics, or marketing data.
  • Experience with marketing data, audience targeting, or campaign analytics preferred.
  • Experience with SQL, Python or R (Alteryx strongly preferred); predictive modeling; statistical analysis; and data integration.
  • Familiarity with cloud-based data environments, AI/ML concepts, and data visualization tools (Tableau, Power BI).
  • Ability to translate data into clear business insights and communicate effectively with technical and non-technical audiences.
  • Strategic, problem-solving mindset with interest in product development.

*Credit and criminal background verification required on all applicants.

B2E Data Marketing is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.