Rodney Murray

Data Scientist/Engineer

Senior Data Scientist with 11+ years of experience and a petroleum engineering background, skilled at architecting scalable data platforms (Snowflake, dbt, AWS) and deploying ML/AI solutions that drive multimillion-dollar impacts. Holds a degree in Chemical Engineering from the University of Wyoming and a Master of Applied Statistics from Colorado State University.

Experiences

Lead Research Engineer

Xecta

2026 - Present | Helena, MT (Remote)

Lead and implement initiatives to research and develope new methodologies to better help customers optimize and surveil their oil and gas assets.

• Implement physics informed machine learning models for coal seam gas wells in Australia with progressive cavity pumps, resulting immediate increases in gas production from an initial pilot set of 30 wells with potential application to over 1,000 wells.
• Implement a physics informed machine learning model for rod pump lifted coal seam gas wells in Australia enabling network modeling of >1,000 wells to identify production bottlenecks.

Senior Data Scientist

ConocoPhillips

2020 - 2025 | Anchorage, AK

Lead data science initiatives across multiple functions to optimize oilfield operations, production, and data infrastructure using advanced analytics, machine learning, and cloud technologies.

• Increased annual revenue by >$9 million through utilization of reinforcement learning with a Python based environment for waterflood optimization.
• Added >$6 million of annual revenue by deploying a neural network combined with a genetic algorithm for optimization of production wells. Developed a robust deployment pipeline integrating the AWS Sagemaker deployment and process control environments for seamless operational use.
• 30x increase in speed of production rate forecasting while maintaining forecast accuracy enabled engineers to analyze access results in near real-time for field analysis. This was accomplished through the development of a deep learning model deployed via Ray for distributed computing and utilizing AWS S3 and Athena for data storage and querying.
• Reduced code volume and complexity by 30% and developed robust CI/CD pipelines for 5 key projects by refactoring Python-based data pipelines to leverage Snowflake and dbt. The effort streamlined processes and established a culture of technical excellence in the team through targeted mentorship and upskilling.
• Projected 40-60 hours/month time savings through implementation of simplified well performance workflows built in SvelteKit and JavaScript.
• Identified and piloted dbt Cloud for Snowflake data transformations, driving corporate approval to standardize dbt workflows with GitHub for version control and robust CI/CD pipelines.

Voidage Management Coordinator

ConocoPhillips

2019 - 2020 | Anchorage, AK

Coordinated waterflood and gas injection operations, leveraging analytics and optimization to maximize production and efficiency across assets.

• >$5 million annual increase in revenue from operational changes. Accomplished by collaborating in a multidisciplinary team to deploy a deep learning network coupled with a genetic algorithm for production optimization analysis by engineers. Published in SPE-201760-MS.
• Implemented custom optimization algorithms to allocate resources for water alternating gas injection conversions leading to improved production and reduced time spent by >4 hrs./week.
• Shared data analysis processes and BI tools for analysis of streamlines simulation results with two international business units resulting in adoption in Norwegian assets.
• Provide data-driven insights to engineers for injector performance through the maintenance and analysis of results from a reservoir simulation model.

Reservoir Engineer

ConocoPhillips

2014 - 2019 | Anchorage, AK

Provided reservoir engineering support for waterflood optimization, project economics, and simulation studies to drive value in mature assets.

• Evaluated economics and received approval for expense projects totaling >$2 million NPV.
• Reduced time to insight of >1000 wells via utilization of reservoir simulation on a HPC cluster.
• Led pattern health reviews resulting in decisions leading to a > $6 million annual revenue increase.

Education

• Master of Applied Statistics

  Colorado State University • GPA: 4.0 • 08/2021 - 05/2024

• B.S. Chemical Engineering

  University of Wyoming • GPA: 3.74 • 08/2010 - 05/2014

Skills

• Python

  Developed machine learning and Bayesian models for production optimization and forecasting using PyMC, scikit-learn, and Keras/TensorFlow.

• Distributed Computing

  Leveraged Ray to accelerate model training and inference, reducing time to insight for large datasets.

• R

  Utilized R to develop specialized mix-models and survival/reliability models

• Rust

  Utilized Rust to optimize lower level computations, in particular for multiphase hydraulics and optimization. Resulted in ~100x reduction in runtime and lower memory usage for multiphase hydraulics calculations. Made Rust functions available for use with Python and Polars using PyO3.

• Docker

  Containerized data science applications for consistent development, testing, and deployment across environments.

• Snowflake

  Architected and optimized cloud data warehouses in Snowflake to support analytics, reporting, and machine learning workflows.

• SQL

  Designed and executed complex SQL queries for data extraction, transformation, and analysis across large-scale datasets.

• dbt

  Designed and managed data transformation workflows in dbt for Snowflake, standardizing analytics engineering and enabling robust version control and CI/CD processes with GitHub.

• AWS (Sagemaker, Athena, S3)

  Integrated S3, Athena, and Sagemaker to build scalable data pipelines, deploy ML models, and enable real-time analytics for oilfield operations.

• CI/CD

  Developed and maintained CI/CD pipelines for automated testing and deployment of data science and analytics projects, ensuring rapid and reliable delivery.

• SvelteKit

  Built interactive web applications and internal tools for engineering workflows using SvelteKit and JavaScript.

• Git

  Used Git, GitHub, and GitLab for version control, collaboration, and CI/CD integration in analytics and engineering projects.

• Terraform

  Provisioned and managed cloud infrastructure as code using Terraform for reproducible and scalable deployments of ML infrastructure.