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Remote Machine Learning Finance Jobs in Oklahoma

Summary We're looking for a Machine Learning Engineer to design, deploy, and operate production ML ... Experience in a regulated industry (gaming, finance, healthcare) where auditability, explainability ...

$13 - $17.50/hr

As an intern, you will learn how to implement novel, cutting-edge remote sensing and machine learning techniques to solve challenging research questions. To efficiently execute your solutions, you ...

Remote micro1 is engaging PhD-level Engineers in Electrical, Mechanical, or Chemical disciplines to ... Experience with or interest in AI, machine learning, or technology-driven projects (a plus, not ...

Remote micro1 is engaging PhD-level Engineers in Electrical, Mechanical, or Chemical disciplines to ... Experience with or interest in AI, machine learning, or technology-driven projects (a plus, not ...

Remote micro1 is engaging PhD-level Engineers in Electrical, Mechanical, or Chemical disciplines to ... Experience with or interest in AI, machine learning, or technology-driven projects (a plus, not ...

Domain Expert - (STEM PhD)

Tulsa, OK ยท Remote

$80 - $90/hr

Remote micro1 is engaging PhD-level Engineers in Electrical, Mechanical, or Chemical disciplines to ... Experience with or interest in AI, machine learning, or technology-driven projects (a plus, not ...

Engineering AI Evaluator (PhD)

Tulsa, OK ยท Remote

$80 - $90/hr

Remote micro1 is engaging PhD-level Engineers in Electrical, Mechanical, or Chemical disciplines to ... Experience with or interest in AI, machine learning, or technology-driven projects (a plus, not ...

Business Analytics Tutor

Stillwater, OK ยท Remote

$18 - $40/hr

Ability to explain statistical modeling techniques, machine learning basics, and business ... finance, and supply chain applications. * Curriculum Awareness & Adaptive Instruction: Familiar ...

Business Analytics Tutor

Tulsa, OK ยท Remote

$18 - $40/hr

Ability to explain statistical modeling techniques, machine learning basics, and business ... finance, and supply chain applications. * Curriculum Awareness & Adaptive Instruction: Familiar ...

$45.25 - $60.50/hr

Exposure to AI, machine learning, or AI-driven software initiatives * Proven ability to lead cross ... LATAM & United States Open to 100% Fully Remote Other Information The work hours will be ...

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Showing results 1-20

Remote Machine Learning Finance information

How do remote machine learning professionals in finance typically collaborate with cross-functional teams?

Remote machine learning professionals in finance often work closely with data analysts, financial experts, and software engineers to develop and deploy predictive models. Collaboration is typically facilitated through virtual meetings, shared documentation, and project management tools. Clear communication and regular check-ins are crucial for aligning goals and ensuring that machine learning solutions address real business needs. Many organizations also encourage participation in virtual workshops and code reviews to maintain a strong sense of teamwork despite the remote setting.

What are the key skills and qualifications needed to thrive as a Remote Machine Learning Finance professional, and why are they important?

To excel in Remote Machine Learning Finance, strong analytical skills, a solid background in statistics or mathematics, and experience with financial data are essential, often supported by a degree in computer science, finance, or a related field. Familiarity with programming languages like Python or R, experience with machine learning frameworks (such as TensorFlow or Scikit-learn), and knowledge of financial modeling tools are typically required. Excellent problem-solving, communication, and the ability to work independently are standout soft skills in this remote environment. These abilities are crucial for developing effective financial models, interpreting complex data, and collaborating with distributed teams to drive business value.

What is a Remote Machine Learning Finance job?

A Remote Machine Learning Finance job involves applying machine learning techniques and algorithms to financial data and problems, often from a remote location. Professionals in this field develop models to predict market trends, assess risks, automate trading, or detect fraud using large datasets. Remote roles allow employees to work from anywhere, collaborating with teams virtually and using cloud-based tools to analyze data. These positions typically require strong programming skills, knowledge of finance, and experience with machine learning frameworks.
What are the most commonly searched types of Machine Learning Finance jobs in Oklahoma? The most popular types of Machine Learning Finance jobs in Oklahoma are:
What are popular job titles related to Remote Machine Learning Finance jobs in Oklahoma? For Remote Machine Learning Finance jobs in Oklahoma, the most frequently searched job titles are:
What cities in Oklahoma are hiring for Remote Machine Learning Finance jobs? Cities in Oklahoma with the most Remote Machine Learning Finance job openings:
Machine Learning Engineer (AWS)

Machine Learning Engineer (AWS)

CCT

Tulsa, OK โ€ข On-site, Remote

Full-time

Re-posted 13 days ago


Job description

Summary
We're looking for a Machine Learning Engineer to design, deploy, and operate production ML systems on Amazon Web Services. You'll own the full lifecycle in a real-world, high-stakes environment - from training and packaging through deployment, monitoring, retraining, security, and cost control.
This role sits at the intersection of ML engineering and MLOps and is core to CCT's analytics strategy. You'll partner closely with data scientists, engineers, and product stakeholders to turn complex time-series and transactional data into reliable, observable, and cost-effective ML services that our customers can trust.
You'll thrive here if you naturally dig into why models behave the way they do, enjoy tracing issues to their root cause, and like collaborating across disciplines to ship robust systems that are built to last.
What You'll Do
  • Build and maintain reproducible model training workflows on AWS (SageMaker, S3, Glue, etc.), making retraining, rollback, and experimentation routine rather than heroic.
  • Deploy and operate real-time and batch inference services with full CI/CD pipelines, versioning, and safe rollout strategies (canary, shadow, A/B) so changes are deliberate and observable.
  • Instrument production models for performance, data drift, latency, and errors - and automate retraining triggers when models drift out of tolerance.
  • Maintain model lineage, auditability, and traceability to meet the compliance, governance, and reporting needs of the regulated gaming industry.
  • Enforce least-privilege IAM, encryption, and secure data access patterns across the entire ML platform.
  • Treat cost as a first-class engineering metric - right-size infrastructure, balance batch vs. real-time workloads, and continually reduce platform spend without sacrificing reliability.
  • Collaborate with engineers, data scientists, and product teams to translate business problems into ML solutions, communicate tradeoffs clearly, and iterate based on feedback.
  • Continuously explore new AWS services, ML frameworks, and deployment patterns to improve reliability, observability, and developer velocity on the ML platform.

Requirements
  • 3+ years of experience in machine learning engineering, MLOps, or a closely related discipline.
  • Hands-on experience with AWS ML and data services - SageMaker (training, endpoints, pipelines), S3, Lambda, Step Functions, CloudWatch, MWAA (Apache Airflow).
  • Experience working with time series data, including feature engineering, seasonality handling, and temporal train/test splits.
  • Strong Python skills and familiarity with common ML frameworks (scikit-learn, PyTorch, XGBoost, or equivalent).
  • Experience building and maintaining CI/CD pipelines for ML systems.
  • Demonstrated ability to monitor and debug production ML systems - latency, drift, errors, and data quality - and drive issues to root cause.
  • Comfort with SQL and working with structured data at scale.
  • Able to work collaboratively across teams, assume positive intent, and communicate clearly with both technical and non-technical stakeholders.
  • Track record of self-directed learning and technical growth in areas like AWS, ML frameworks, or deployment patterns.

Certified Banana Picker
Nice to Have
  • Experience in a regulated industry (gaming, finance, healthcare) where auditability, explainability, and compliance are first-class concerns.
  • Familiarity with feature stores, model registries, or ML metadata tools (e.g., MLflow, SageMaker Model Registry).
  • Experience with infrastructure-as-code (Terraform, CDK, or CloudFormation).
  • Exposure to data drift detection libraries or custom drift monitoring implementations.

Success Looks Like
  • Production models run reliably with clear, measurable business impact for casino operators.
  • Failures are observable, recoverable, and explainable - with logs, metrics, and traces that tell the full story.
  • ML systems scale predictably with usage and data volume, without runaway cost.
  • The ML platform becomes a trusted, well-understood part of CCT's product ecosystem - for both internal teams and external customers.

About CCT
CCT is the creator of Casino Insightโ„ข, the award-winning platform trusted by more than 350 casinos worldwide to automate cage operations, revenue audits, and operational analysis. Since 2012, Casino Insight has helped casinos replace manual work with streamlined workflows, improving accuracy, compliance, and profitability.
Headquartered in Tulsa, Oklahoma, CCT integrates seamlessly with leading casino management, hospitality, and financial systems-delivering measurable ROI and empowering teams to work smarter at every level.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.