ML Ops Engineer

Murphy USA is investing in artificial intelligence, machine learning, data platforms, and engineering practices that turn data science work into measurable business value. We are looking for a Data Science MLOps associate to help bridge data science, data engineering, and cloud operations. They will build reliable, secure, scalable capabilities that move data science solutions from development into governed production use.

The Data Science MLOps Associate will design, build, deploy, monitor, and continuously improve the systems, pipelines, tools, and standards required to operationalize machine learning, artificial intelligence, statistical models, optimization solutions, and advanced analytics products. This role combines data engineering, data science, Dev Ops, and cloud platform practices. The successful individual will work closely with data scientists, data engineers, business partners, architecture, security, infrastructure, and application teams.

They will ensure solutions are reproducible, automated, well-documented, observable, secure, performant, and aligned to business outcomes. This position is expected to contribute hands‑on to model development support, feature engineering pipelines, model training and deployment workflows, experiment tracking, model registry practices, CI/CD automation, monitoring, incident response, data quality validation, performance and optimization.

NOTE:
This role is based in El Dorado, AR.

• Design, build, maintain, and improve production-ready MLOps capabilities that support the full machine learning lifecycle, including data preparation, feature engineering, experiment tracking, model training, model evaluation, model packaging, deployment, monitoring, retraining, and retirement.
• Partner with data scientists to convert research notebooks, prototypes, statistical models, optimization models, and machine learning solutions into reliable, maintainable, tested, version-controlled, and production-ready code.
• Build and operate scalable data and feature pipelines using modern data engineering practices, including Python, SQL, Databricks, cloud services, orchestration tools, reusable libraries, automated validation, metadata capture, and clear documentation.
• Develop and maintain CI/CD pipelines for machine learning and analytical solutions, including automated testing, code quality checks, deployment workflows, environment promotion, rollback planning, and release documentation.
• Implement model deployment patterns appropriate to business needs, including batch scoring, scheduled jobs, APIs, streaming or near-real-time inference, embedded analytics, and integration with downstream applications or reporting workflows.
• Establish monitoring and observability for production models and data science products, including data quality checks, model performance metrics, drift detection, error tracking, service health, pipeline reliability, usage patterns, cost, and alerting.
• Support operational excellence for data science solutions through incident response, root cause analysis, remediation planning, preventive maintenance, service-level expectations, runbooks, and continuous improvement.
• Create reusable templates, frameworks, libraries, standards, and documentation that accelerate model development and production deployment while improving consistency, reliability, security, and maintainability across the Data Science team.
• Embed practical governance into data science delivery, including model versioning, data lineage, access control, auditability, documentation, approval workflows, security requirements, privacy considerations, and compliance with enterprise technology standards.
• Collaborate with data engineering teams to ensure analytical datasets, features, training data, and production scoring inputs are reliable, trusted, timely, performant, and aligned with enterprise data architecture.
• Communicate solution options, tradeoffs, risks, delivery status, operational issues, and recommendations clearly to both technical and non-technical stakeholders.
• Improve Data Science delivery practices by promoting source control, peer review, automated testing, reproducible environments, modular code, documentation, observability, Dev Ops discipline, and production support readiness.
• Support responsible use of AI and machine learning by applying appropriate controls for explainability, monitoring, validation, human review, secure data handling, and…