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Scientific Machine Learning Jobs in Utah (NOW HIRING)

Data Scientist

Lehi, UT ยท On-site

The data scientist role involves solving technical, data-driven Healthcare problems using computer ... The role will focus on extending machine learning, predictive modeling, and analytic components to ...

The role will focus on extending machine learning, predictive modeling, and analytic components to provide up-to-date intelligence to Healthcare providers maximizing outcomes. An ideal candidate for ...

Currently, We are looking for entry-level software programmers, Java full-stack developers, Python/Java developers, Data analysts/ Data Scientists, and Machine Learning engineers for full-time ...

The data scientist role involves solving technical, data-driven Healthcare problems using computer ... The role will focus on extending machine learning, predictive modeling, and analytic components to ...

The data scientist role involves solving technical, data-driven Healthcare problems using computer ... The role will focus on extending machine learning, predictive modeling, and analytic components to ...

The data scientist role involves solving technical, data-driven Healthcare problems using computer ... The role will focus on extending machine learning, predictive modeling, and analytic components to ...

... building large-scale machine learning, predictive modeling, and advanced analytics tools ... Qualifications : Required : โ€ข Undergraduate degree in Data Science, Statistics, Mathematics ...

Senior Data Scientist

Lehi, UT ยท On-site

$107K - $183K/yr

As a Senior Data Scientist, you will collaborate with cross-functional stakeholders to identify ... Proficient creating machine learning, predictive modeling, and advanced analytics tools tailored to ...

As a Senior Data Scientist, you will collaborate with cross-functional stakeholders to identify ... Proficient creating machine learning, predictive modeling, and advanced analytics tools tailored to ...

As an Applied AI Scientist , you will collaborate with the Enterprise Data Analytics team to ... Design and build analytical and machine learning models * Prototype and validate solutions

As an Applied AI Scientist , you will collaborate with the Enterprise Data Analytics team to ... Design and build analytical and machine learning models * Prototype and validate solutions

Data Science Tutor

Logan, UT ยท Remote

$18 - $40/hr

What We Look For In a Data Science Tutor * Advanced Subject Mastery ... Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ...

Data Science Tutor

Cedar City, UT ยท Remote

$18 - $40/hr

What We Look For In a Data Science Tutor * Advanced Subject Mastery ... Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ...

Data Science Tutor

Spanish Fork, UT ยท Remote

$18 - $40/hr

What We Look For In a Data Science Tutor * Advanced Subject Mastery ... Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ...

AI Engineer

Draper, UT ยท On-site

Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related quantitative field * 2+ years of hands-on experience in data science, machine learning engineering, or applied ...

Data Science Tutor

Provo, UT ยท Remote

$18 - $40/hr

What We Look For In a Data Science Tutor * Advanced Subject Mastery ... Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ...

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Showing results 1-20

Scientific Machine Learning information

Is ML a high paying job?

Scientific Machine Learning roles typically offer high salaries due to the specialized skills required, such as expertise in deep learning, data analysis, and programming with tools like Python and TensorFlow. Compensation varies by industry, experience, and location but generally exceeds average tech salaries for comparable roles.

Which 3 jobs will survive AI?

Scientific Machine Learning professionals will likely continue to be in demand due to their expertise in developing and applying AI models to complex scientific problems. Roles such as data scientists, AI researchers, and machine learning engineers are expected to persist because they require specialized knowledge, critical thinking, and ongoing innovation that AI tools complement rather than replace. These jobs often involve interdisciplinary skills, programming, and understanding of domain-specific data, making them more resilient to automation.

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.

How much does a machine learning scientist make?

A machine learning scientist typically earns between $90,000 and $150,000 annually, depending on experience, education, and location. Senior roles or those with specialized skills in deep learning or natural language processing can earn higher salaries, often exceeding $180,000.

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.

Is 40 too late for data science?

Scientific Machine Learning roles often value skills and experience over age, and many professionals transition into data science or machine learning at various stages of their careers. Learning relevant tools like Python, TensorFlow, or scikit-learn and gaining practical experience can help regardless of age, making 40 not too late to pursue this field.
What cities in Utah are hiring for Scientific Machine Learning jobs? Cities in Utah with the most Scientific Machine Learning job openings:
Infographic showing various Scientific Machine Learning job openings in Utah as of June 2026, with employment types broken down into 3% As Needed, 71% Full Time, 23% Part Time, and 3% Contract. Highlights an 84% Physical, 3% Hybrid, and 13% Remote job distribution.
Data Scientist

Data Scientist

Waystar

Lehi, UT โ€ข On-site

Full-time

Medical, Retirement, PTO

Posted 25 days ago


Job description

ABOUT THIS POSITION
Designs, develops and programs methods, processes, and systems to consolidate and analyze unstructured, diverse "big data" sources to generate actionable insights and solutions for client services and product enhancement. Interacts with product and service teams to identify questions and issues for data analysis and experiments. Develops and codes software programs, algorithms and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources. Identifies meaningful insights from large data and metadata sources; interprets and communicates insights and findings from analysis and experiments to product, service, and business managers. Incumbents whose primary role is technical and focused on data storage, warehousing and systems architecture should be matched to Database Engineering or Storage Engineering. Incumbents whose focus is the quantitative analysis of complex business problems and issues using data from internal and external sources to provide insight to decision-makers should be matched to Business Intelligence. Incumbents whose primary role is technical and focused on designing and building system-generated reports, reporting tools and dashboards for data generation should be matched to Data Informatics. Incumbents whose focus is primarily on experimental design and advanced or complex statistical analysis and modeling of datasets should be matched to Statistician/Mathematician. This is a product engineering role in which employees work with multiple types of business data. Incumbents whose focus is primarily on analysis and modeling of financial, marketing or pricing data should be matched to Finance, Market Research or Pricing as appropriate. May be internal operations-focused or external client-focused, working in conjunction with Professional Services and outsourcing functions.
WHAT YOU'LL DO
Job Description
We are looking for an experienced Data Scientist, who has previously supported Healthcare software applications. The data scientist role involves solving technical, data-driven Healthcare problems using computer science, mathematical, predictive modeling and statistical methods, & knowledge. This person will interact with teams from Account Management to Application Engineering and R&D, to conduct detailed analysis and experimentation to maximize the utility of predictive modeling, analytic and machine learning across Waystar's product line. The role will focus on extending machine learning, predictive modeling, and analytic components to provide up-to-date intelligence to Healthcare providers maximizing outcomes. An ideal candidate for this position can approach problem-solving challenges independently, has a strong attention to detail, and enjoys working in a fast-paced, collaborative, and team-based environment.
  • Additional Job Description
    • Complete familiarity with various statistical and machine learning techniques including: classification, regression, dimension reduction, clustering, and various multivariate methods
    • Complete familiarity with empirical approaches to estimate performance of machine learning models including: hold-out sets, cross-validation, leave-one-out testing
    • Understanding of orders of algorithms and how they scale
    • Demonstrated competency in R/Python predictive modeling
    • Demonstrated competency in RDBMS (e.g. SQL Server)
    • Ability to code in 1+ general purpose programming language (C#, Java, etc.)
    • Must be a quick-learner with the ability to multi-task in a fast-paced environment
    • Outstanding presentation abilities and the ability to communicate with all levels of the Business
    • Comfortable working in newly-forming, ambiguous areas where learning and adaptability are key skills
    • Outstanding communication and interpersonal skills
    • Must possess strong analytical, problem-solving, and writing skills
    • Proficient in Microsoft Office applications
    • Detail-oriented
    • Master of Science degree or higher in the fields of Computer Science, Statistics, or Mathematics is preferred
    • An aptitude for medical informatics is preferred

WHAT YOU'LL NEED
Additional Job Description
  • Works closely with Application Engineering, Product Management, and Operational teams in designing, experimenting-with, and implementing machine learning and analytical systems applied to design information and user behavior
  • Works closely with Application Engineering teams to gather and process data, as well as in surfacing various analytically-based features in core products
  • Works on groundbreaking new applications of machine learning and analytic technology to Healthcare, producing quantitative, justifiable results to guide feature planning
  • Translates real-world Healthcare problems to mathematical frameworks
  • Works with Product Management, Marketing, and Sales as needed to promote sales and to incorporate market and customer feedback
  • Data exploration, hypothesis creation (from business and product goals), testing algorithms, scaling to large data-sets and validating results will be common tasks for this role
  • Understands, organizes, and communicates root causes of problems and successes succinctly

ABOUT WAYSTAR
Through a smart platform and better experience, Waystar helps providers simplify healthcare payments and yield powerful results throughout the complete revenue cycle.
Waystar's healthcare payments platform combines innovative, cloud-based technology, robust data, and unparalleled client support to streamline workflows and improve financials so providers can focus on what matters most: their patients and communities. Waystar is trusted by 1M+ providers, 1K+ hospitals and health systems, and is connected to over 5K commercial and Medicaid/Medicare payers. We are deeply committed to living out our organizational values: honesty; kindness; passion; curiosity; fanatical focus; best work, always; making it happen; and joyful, optimistic & fun.
Waystar products have won multiple Best in KLASยฎ or Category Leader awards since 2010 and earned multiple #1 rankings from Black Bookโ„ข surveys since 2012. The Waystar platform supports more than 500,000 providers, 1,000 health systems and hospitals, and 5,000 payers and health plans. For more information, visit waystar.com or follow @Waystar on Twitter.
WAYSTAR PERKS
  • Competitive total rewards (base salary + bonus, if applicable)
  • Customizable benefits package (3 medical plans with Health Saving Account company match)
  • We offer generous paid time off for our non-exempt team members, starting with 3 weeks + 13 paid holidays, including 2 personal floating holidays. We also offer flexible time off for our exempt team members + 13 paid holidays
  • Paid parental leave (including maternity + paternity leave)
  • Education assistance opportunities and free LinkedIn Learning access
  • Free mental health and family planning programs, including adoption assistance and fertility support
  • 401(K) program with company match
  • Pet insurance
  • Employee resource groups

Waystar is proud to be an equal opportunity workplace. We celebrate, value, and support diversity and inclusion. Qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, national origin, disability status, genetics, marital status, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.
This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.