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Remote Healthcare Machine Learning Jobs in St Louis, MO

Koantek

Senior AI Engineer

Chesterfield, MO · Remote

$54.75 - $70.50/hr

This remote role requires a blend of advanced Machine Learning (ML) expertise, deep knowledge of MLOps principles, and a proven track record in client-facing implementation. The successful candidate ...

Bunge North America

Data Scientist

Chesterfield, MO · On-site +1

Translate complex business problems into data-driven analytics and machine learning tasks, then ... Experience with geospatial data analysis, remote sensing, satellite imagery processing and deep ...

Bunge

Data Scientist

Chesterfield, MO · On-site +1

Translate complex business problems into data-driven analytics and machine learning tasks, then ... Experience with geospatial data analysis, remote sensing, satellite imagery processing and deep ...

GRVTY

Imagery Scientist (EO) - Senior

Saint Louis, MO · On-site +1

$160K - $190K/yr

Experience applying CV and machine learning (ML) techniques to EO imagery and data to address ... healthcare, financial wellness, retirement planning, family assistance, continued education, and ...

Varsity Tutors

Data Science Tutor

Saint Louis, MO · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... and healthcare informatics. * Curriculum Awareness & Adaptive Instruction: Familiar with data ...

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All JobsHealthcare Machine Learning JobsRemote Healthcare Machine Learning JobsRemote Healthcare Machine Learning Jobs in St. Louis, MO

Remote Healthcare Machine Learning information

See St Louis, MO salary details

$24.8K

$41.4K

$85.6K

How much do remote healthcare machine learning jobs pay per year?

As of Sep 7, 2026, the average yearly pay for remote healthcare machine learning in St. Louis, MO is $41,401.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,600.00 and $44,700.00 per year, depending on experience, location, and employer.

What is a remote healthcare machine learning professional?

A Remote Healthcare Machine Learning professional is someone who applies machine learning techniques and data analysis to healthcare-related problems while working remotely. They develop algorithms and models to analyze medical data, predict patient outcomes, and improve healthcare delivery. These professionals may work on projects like disease prediction, medical imaging analysis, or personalized treatment recommendations, often as part of a distributed team. Their work helps healthcare organizations leverage data to make informed decisions and improve patient care, all while working from a location outside of a traditional office or hospital setting.

How does a remote healthcare machine learning professional collaborate with clinical teams to implement AI solutions?

Remote Healthcare Machine Learning professionals often work closely with clinicians, data engineers, and IT staff to ensure that AI models address real clinical needs and comply with healthcare regulations. Collaboration usually involves regular virtual meetings, shared project management tools, and iterative feedback cycles where clinicians provide insights on data relevance and model outputs. Effective communication is crucial to bridge the gap between technical and medical expertise, ensuring solutions are both accurate and practical for everyday clinical use.

What are the key skills and qualifications needed to thrive as a remote healthcare machine learning specialist?

To thrive as a Remote Healthcare Machine Learning Specialist, you need a strong background in data science, statistics, machine learning algorithms, and healthcare domain knowledge, typically supported by a relevant degree in computer science, engineering, or biomedical informatics. Proficiency with programming languages (such as Python or R), machine learning frameworks (like TensorFlow or PyTorch), and experience with electronic health record (EHR) systems or health data standards is essential. Strong problem-solving skills, attention to detail, and the ability to communicate complex technical concepts to non-technical stakeholders make someone stand out in this role. These skills are crucial for developing effective, compliant, and impactful healthcare solutions that improve patient outcomes and enable remote care delivery.

What is the difference between Remote Healthcare Machine Learning vs Remote Healthcare Data Analyst?

AspectRemote Healthcare Machine LearningRemote Healthcare Data Analyst
Required CredentialsDegree in Computer Science, Data Science, or related field; knowledge of ML algorithmsDegree in Statistics, Data Analysis, or related field; proficiency in data visualization
Work EnvironmentCollaborates with data scientists and engineers; focuses on developing modelsAnalyzes healthcare data; reports insights to stakeholders
Industry UsageDevelops predictive models for patient outcomes, diagnosticsInterprets healthcare data to inform decisions and improve processes

Remote Healthcare Machine Learning specialists focus on creating algorithms and models to predict health trends, while Remote Healthcare Data Analysts interpret healthcare data to support decision-making. Both roles require strong analytical skills but differ in technical focus and responsibilities.

What are popular job titles related to Remote Healthcare Machine Learning jobs in St. Louis, MO?

For Remote Healthcare Machine Learning jobs in St. Louis, MO, the most frequently searched job titles are:

What job categories do people searching Remote Healthcare Machine Learning jobs in St. Louis, MO look for?

The top searched job categories for Remote Healthcare Machine Learning jobs in St. Louis, MO are:

What cities near St. Louis, MO are hiring for Remote Healthcare Machine Learning jobs?

Cities near St. Louis, MO with the most Remote Healthcare Machine Learning job openings:

Remote Healthcare Machine Learning jobs near you
Infographic showing various Remote Healthcare Machine Learning job openings in St. Louis, MO as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% Remote job distribution, with an average salary of $41,401 per year, or $19.9 per hour.

Senior AI Engineer

Koantek

Chesterfield, MO • Remote

$54.75 - $70.50/hr

Contractor

Re-posted 5 days ago

Key responsibilities

  • Lead and execute end-to-end ML project implementations directly with clients, translating business problems into technical solutions.

  • Design, build, and maintain production-grade ML pipelines with a focus on CI/CD and MLOps practices to ensure model reliability and scalability.

  • Implement and optimize Generative AI and NLP applications, including technologies like RAG and LLMs, in a production environment.

Job description

Sr AI Engineer / Data Scientist / MLOps Consultant Location: United States - Remote Employment Type: Full-Time and Contract We are seeking an experienced and highly technical Data Scientist to join our customer-facing consulting team. This remote role requires a blend of advanced Machine Learning (ML) expertise, deep knowledge of MLOps principles, and a proven track record in client-facing implementation. The successful candidate will be instrumental in designing, deploying, and maintaining production-grade ML solutions, including advanced Generative AI and Natural Language Processing (NLP) models, for our diverse client base.Key Responsibilities Serve as a primary technical consultant, leading and executing end-to-end ML project implementations directly with clients, translating complex business problems into robust technical solutions

Exhibit excellent communication, presentation, and stakeholder management skills to clearly articulate technical findings, proposals, and project status to both technical and non-technical audiences. Design, build, and maintain production-grade ML pipelines, focusing on continuous integration, continuous delivery (CI/CD), and advanced MLOps practices to ensure reliability and scalability of models. Implement and optimize cutting-edge Generative AI and NLP applications, demonstrating hands-on experience with technologies like Retrieval Augmented Generation (RAG) and Large Language Models (LLMs) in a production setting.

Manage underlying solution infrastructure, demonstrating proficiency in technologies such as Docker, pipeline orchestrators, and database systems. Leverage expertise in distributed computing frameworks, specifically in scalable machine learning and high-performance data processing (e.g., using technologies like Apache Spark). Contribute to the strategic growth of the ML Practice Team, including participation in technical assignments and knowledge transfer activities

Ensure all client engagements and training activities are properly documented and reported via designated partner platforms. Required Qualifications 4+ years of hands-on professional experience developing, deploying, and managing Machine Learning models, with a mandatory requirement for productionizing and maintaining models in a live environment. 3+ years of experience in a customer-facing consulting or solutions architect role, focused on technical implementation and delivery.

Excellent verbal and written communication skills for effective client and internal team interaction. Expertise in MLOps lifecycle management, including model versioning, testing, monitoring, and automated deployment best practices. Demonstrable experience with infrastructure management, encompassing containerization (Docker) and data pipeline orchestration.

Deep understanding of programming for data-intensive and scalable ML applications. Proven experience in deploying and managing Generative AI and NLP solutions for client applications. Preferred Qualifications Hands-on experience with modern ML platform stacks, such as Databricks MLOps Stacks.

Knowledge of specific tools and techniques used in scalable machine learning and large-scale data processing. Demonstrated commitment to continuous learning in emerging ML fields, such as LLMs and GenAI application architectures. Requirements Hands-on experience with modern ML platform stacks, such as Databricks MLOps Stacks.

Knowledge of specific tools and techniques used in scalable machine learning and large-scale data processing. Demonstrated commitment to continuous learning in emerging ML fields, such as LLMs and GenAI application architectures.

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