ML Ops Engineer I

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, DevOps, 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, DevOps 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 alignment with business objectives.
• Perform any other related duties as required or assigned.

• Demonstrated experience building, deploying, supporting, or productionizing data science, machine learning, AI, optimization, advanced analytics, or data engineering solutions.
• Hands-on proficiency with Python, SQL, source control, code review practices, package management, testing frameworks, and software engineering practices used to build maintainable production code.
• Experience with cloud-based data and analytics platforms, preferably Azure and Databricks, including notebook development, jobs or workflows, compute management, scalable data processing, and production scheduling.
• Experience with MLOps or DevOps practices such as CI/CD, automated testing, artifact management, environment management, release management, infrastructure automation, monitoring, alerting, and operational support.
• Working knowledge of machine learning concepts, model development workflows, feature engineering, model evaluation, experiment tracking, model versioning, model deployment, retraining approaches, and performance monitoring.
• Experience designing and maintaining data pipelines, feature pipelines, analytical datasets, data quality checks, ETL/ELT processes, and integration patterns that support analytics and model production workloads.
• Ability to troubleshoot complex issues across code, data, dependencies, environments, cloud services, pipeline orchestration, model behavior, and downstream business processes.
• Ability to translate business requirements and data science objectives into scalable technical solutions that are reliable, secure, cost-conscious, reproducible, and maintainable.
• Strong documentation skills, including the ability to create runbooks, deployment guides, technical specifications, architecture notes, model documentation, validation summaries, and operational support materials.
• Strong written and verbal communication skills, including the ability to explain technical concepts, risks, model behavior, deployment options, and operational tradeoffs to technical and non-technical audiences.
• Knowledge of MLflow, model registries, feature stores, orchestration tools, Docker, Kubernetes, REST APIs, Azure DevOps, GitHub, Terraform, or similar MLOps and platform tools is preferred but not required.
• Experience supporting analytics, AI/ML, retail, convenience, fuel, supply chain, pricing, merchandising, operations, customer, or other high-volume transactional business problems is preferred.

Broad knowledge of data science, machine learning, data engineering, software development, cloud platforms, database technologies, analytics, and DevOps practices. Equivalent to a four-year college degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Management Information Systems, Analytics, or a related quantitative or technical field, plus some related experience and/or training in data science, data engineering, machine learning, analytics engineering, software engineering, DevOps, cloud engineering, or MLOps; or an equivalent combination of education and experience.

This role is accountable for the technical quality, production readiness, reliability, maintainability, and operational supportability of assigned data science and MLOps deliverables. The Data Science MLOps Engineer recommends implementation approaches, deployment patterns, testing strategies, monitoring requirements, operational controls, and technical improvements within established architecture, security, governance, and platform standards. The role makes day-to-day technical decisions related to code structure, pipeline design, automation, validation, documentation, observability, deployment readiness, and issue remediation. Decisions with material business, architectural, security, cost, vendor, or enterprise platform impact are escalated and aligned with Data Science leadership, architecture, security, infrastructure, data engineering, and business stakeholders as appropriate.

$72,500 - $93,900