1

Scientific Machine Learning Jobs in Missouri (NOW HIRING)

As a Senior Data Scientist , you will drive both analytical insight and technical model development ... Develop Risk Mitigation Models using machine learning, anomaly detection, and statistical ...

We're looking for a strategic Data Science leader who can turn complex business challenges into scalable AI, machine learning, and analytics solutions that drive measurable business impact. In this ...

(USA) Senior, Data Scientist

Noel, MO · On-site

$90K - $180K/yr

As a Senior Data Scientist , you will drive both analytical insight and technical model development ... Develop Risk Mitigation Models using machine learning, anomaly detection, and statistical ...

Group Director, Data Science

Anderson, MO · On-site

$195K - $370K/yr

We're looking for a strategic Data Science leader who can turn complex business challenges into scalable AI, machine learning, and analytics solutions that drive measurable business impact. In this ...

Group Director, Data Science

Noel, MO · On-site

$195K - $370K/yr

We're looking for a strategic Data Science leader who can turn complex business challenges into scalable AI, machine learning, and analytics solutions that drive measurable business impact. In this ...

LRS Consulting is seeking a Senior Machine Learning Engineer to design, build, and scale production ... Transform data science prototypes into production-ready solutions using appropriate ML and GenAI ...

The Senior Director, Data Science & Ontology will define and scale Walmart's semantic intelligence ... Ensure teams use appropriate statistical, machine learning, optimization, and AI techniques to ...

next page

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 are popular job titles related to Scientific Machine Learning jobs in Missouri? For Scientific Machine Learning jobs in Missouri, the most frequently searched job titles are:
What cities in Missouri are hiring for Scientific Machine Learning jobs? Cities in Missouri with the most Scientific Machine Learning job openings:
Infographic showing various Scientific Machine Learning job openings in Missouri as of June 2026, with employment types broken down into 78% Full Time, and 22% Part Time. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution.
(USA) Senior, Data Scientist

(USA) Senior, Data Scientist

Walmart

Cassville, MO

$90K - $180K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 23 hours ago


Walmart rating

6.0

Company rating: 6.0 out of 10

Based on 21,743 frontline employees who took The Breakroom Quiz

22nd of 39 rated national retailers


Job description

Position Summary...What you'll do...Walmart’s Decision Management Team supports the growth of the e-Commerce Marketplace program through the practical application of data science and advanced analysis to optimize risk decision strategies.  This includes data analysis, advanced statistics, case investigation and application of advanced modeling techniques to manage risk on the ecommerce platform. We work alongside business, product, and engineering teams to deliver solutions to manage Marketplace risk.
  What You'll Do…
As a Senior Data Scientist, you will drive both analytical insight and technical model development to detect and mitigate fraud and performance risks. You’ll work on high-impact problems that require a mix of rigorous analysis, experimentation, and machine learning. This role is ideal for someone who is comfortable moving between deep analytics and building scalable models in production. How You'll Make an Impact:
  • Develop Risk Mitigation Models using machine learning, anomaly detection, and statistical techniques to identify and respond to emerging risk trends on the Marketplace platform.
  • Analyze Risk Patterns by exploring large datasets to uncover emerging fraud tactics, behavioral anomalies, and areas of business exposure.
  • Develop Predictive Models using supervised and unsupervised learning techniques to power risk detection systems at scale.
  • Lead Deep-Dive Analytics to inform risk policy, product decisions, or operational strategies—identifying key drivers, trends, and optimization opportunities.
  • Integrate Agentic AI Frameworks to support semi-autonomous risk detection systems that can adapt and respond to evolving fraud patterns in real-time.
  • Explore Generative AI (GenAI) Applications for synthetic data generation, anomaly simulation, or scenario modeling to improve risk model robustness.
  • Collaborate Cross-Functionally with product, engineering, and operations teams to ensure seamless integration of data science solutions into business workflows.
  • Monitor Model Performance and proactively identify areas for improvement using quantitative metrics, feedback loops, and model diagnostics.
  • Bridge Analytics and Science by combining robust data analysis with model development to create solutions that are both explainable and effective.
  • Translate Insights into Action by communicating complex analytical findings clearly to both technical and non-technical stakeholders.
  • Design and Execute Experiments (e.g., A/B tests, statistical validations) to evaluate model impact and improve decision strategies.
What You'll Bring:
  • Strong understanding of machine learning, data exploration, statistical modeling, and their application to risk and fraud detection in digital marketplaces.
  • Demonstrated experience developing and deploying models to identify anomalous behavior, detect fraud, or assess risk in real-time systems.
  • Proficiency in Python, SQL, and data science libraries/frameworks such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
  • Familiarity with Agentic AI systems or AI agents—understanding of how autonomous models can be leveraged in dynamic decision environments.
  • Experience conducting experimentation and model validation using rigorous statistical methods (e.g., A/B testing, ROC/AUC, precision/recall).
  • A strong problem-solving mindset and ability to translate ambiguous business problems into clear analytical frameworks.
  • Excellent written and verbal communication skills, with the ability to explain technical concepts to diverse audiences.
  • Experience with data visualization tools (e.g., Tableau, Power BI, matplotlib, seaborn) to craft compelling insights.
Minimum Qualifications:
  • Option 1: Bachelor’s degree in Statistics, Computer Science, Data Science, Mathematics, or related field, with 5+ years of hands-on experience in data science, machine learning, or risk management.
  • Option 2: Master’s degree in a related field (e.g., Data Science, Machine Learning, Statistics, Applied Mathematics) with at least 3+ years of applied experience working on data-driven risk management or fraud prevention.
  • Option 3: 6-8 years of direct experience in data science, machine learning, or applied risk management within an e-commerce or marketplace setting.
Preferred Qualifications:
  • Expertise in using advanced machine learning techniques such as deep learning, reinforcement learning, or anomaly detection for fraud detection or risk mitigation.
  • Understanding of LLMs, agentic workflows, or prompt engineering concepts in the context of AI-enhanced decision systems.
  • Experience with big data technologies like Apache Spark, Hadoop, and cloud-based data solutions (e.g., AWS, Google Cloud) to build scalable risk management platforms.
  • Proficiency in data manipulation and analysis tools such as Pandas, NumPy, and SQL for data wrangling, feature engineering, and analysis.
  • Strong background in model evaluation techniques including ROC/AUC, confusion matrices, precision/recall, and F1 scores, as well as experience with A/B testing and model validation.

At Walmart, we offer competitive pay as well as performance-based bonus awards and other great benefits for a happier mind, body, and wallet. Health benefits include medical, vision and dental coverage. Financial benefits include 401(k), stock purchase and company-paid life insurance. Paid time off benefits include PTO (including sick leave), parental leave, family care leave, bereavement, jury duty, and voting. Other benefits include short-term and long-term disability, company discounts, Military Leave Pay, adoption and surrogacy expense reimbursement, and more. You will also receive PTO and/or PPTO that can be used for vacation, sick leave, holidays, or other purposes. The amount you receive depends on your job classification and length of employment. It will meet or exceed the requirements of paid sick leave laws, where applicable. For information about PTO, see https://one.walmart.com/notices. Live Better U is a Walmart-paid education benefit program for full-time and part-time associates in Walmart and Sam's Club facilities. Programs range from high school completion to bachelor's degrees, including English Language Learning and short-form certificates. Tuition, books, and fees are completely paid for by Walmart.
Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to a specific plan or program terms.
For information about benefits and eligibility, see One.Walmart.
The annual salary range for this position is $90,000.00 - $180,000.00 Additional compensation includes annual or quarterly performance bonuses. Additional compensation for certain positions may also include :
- Stock

‎ 

Minimum Qualifications...

Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.

Option 1- Bachelor’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field and 3 years' experience in an analytics related field. Option 2- Master’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field and 1 years' experience in an analytics related field. Option 3 - 5 years' experience in an analytics or related field.Preferred Qualifications...

Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications.

Data science, machine learning, optimization models, Master’s degree in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch), We value candidates with a background in creating inclusive digital experiences, demonstrating knowledge in implementing Web Content Accessibility Guidelines (WCAG) 2.2 AA standards, assistive technologies, and integrating digital accessibility seamlessly. The ideal candidate would have knowledge of accessibility best practices and join us as we continue to create accessible products and services following Walmart’s accessibility standards and guidelines for supporting an inclusive culture.Primary Location...1601 SE 10th St, Bentonville, AR 72716, United States of AmericaWalmart and its subsidiaries are committed to maintaining a drug-free workplace and has a no tolerance policy regarding the use of illegal drugs and alcohol on the job. This policy applies to all employees and aims to create a safe and productive work environment.

What Walmart employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


Walmart logo

About Walmart

Sourced by ZipRecruiter

From our humble beginnings as a small discount retailer in Rogers, Ark., Walmart has opened thousands of stores in the U.S. and expanded internationally. Through innovation, we're creating a seamless experience to let customers shop anytime and anywhere online and in stores. We are creating opportunities and bringing value to customers and communities around the globe. Walmart operates approximately 10,500 stores and clubs in 19 countries and eCommerce websites. We employ 2.1 million associates around the world — nearly 1.6 million in the U.S. alone.

Industry

Retail and transportation and warehousing

Company size

10,000+ Employees

Headquarters location

Bentonville, AR, US

Social media