Job description

Senior Data Scientist, Geoscience & Engineering The Opportunity The Senior Data Scientist is a key contributor to SM Energy’s mission of applying advanced analytics, machine learning, and data-driven solutions to complex business, engineering, and geoscience challenges. This role identifies high-value opportunities, determines appropriate datasets and algorithms, uncovers patterns, and delivers solutions that improve insight, decision-making, workflows, and automation. This is a high-visibility role at the center of a transformative analytics initiative that will shape the company’s future direction. You will have the opportunity to work on projects of strategic significance, with the potential to influence decisions at the highest levels of the organization. We are seeking versatile individuals who thrive in exploratory, boundary-pushing work and who are motivated by the challenge—and reward—of tackling open-ended problems that can drive lasting impact. What You'll Do (The Nitty-Gritty) • Own the Lifecycle: Drive data science projects from initial concept and data exploration through to enterprise-scale deployment and long-term monitoring. • Uncover Hidden Value: Wrangle and mine large-scale engineering and geologic datasets, fusing disparate sources to identify critical insights, correlations, and patterns. • Build Predictive Engines: Design, develop, and evaluate innovative machine learning and statistical models to solve our most complex business problems, leveraging everything from traditional statistics to deep learning. • Deploy to Production: Architect and implement robust model deployment solutions for batch, real-time, and edge-computing use cases, ensuring your work creates tangible business value. • Collaborate to Innovate: Work shoulder-to-shoulder with data engineers, DevOps engineers, and front-end developers to build the full-stack infrastructure needed to support your solutions. • Champion Data Science: Act as a thought leader, staying ahead of industry trends (including Generative AI and MLOps), and help cultivate ML skills and a data-driven mindset across the organization. Who You Are • An Innovator: You constantly challenge the status quo, introduce new ways of looking at problems, and are passionate about finding a better way. • A Pragmatic Problem-Solver: You thrive in ambiguity and are comfortable making decisions and moving forward without the total picture. You handle complexity with a calm, composed, and constructive approach. • An Owner: You take initiative, manage multiple projects with autonomy, and proactively eliminate roadblocks to meet deadlines and deliver results. • A Collaborator: You listen effectively, build strong relationships, and can clearly articulate complex technical concepts to both technical and non-technical stakeholders. • Domain-Curious: You have a solid grasp of data science and a strong aptitude for or experience in oil and gas fundamentals, including well performance drivers, reservoir evaluation, and industry economics. Your Technical Toolkit • Languages & Libraries: Expert-level Python and its data ecosystem (Pandas, NumPy, Dask) and ML libraries (Scikit-learn, PyCaret, TensorFlow, PyTorch). • Data & Databases: Proficient in SQL for complex data analysis. Experience with cloud data warehouses (Snowflake) and data virtualization (Denodo) is a strong plus. • Tools & Platforms: • Experience with Jupyter Notebooks, IDEs (PyCharm/VS Code), and version control (Git). • Hands-on experience in a cloud environment (Azure preferred). • Proficiency with MLOps principles and tools. • Visualization: Experience creating compelling data visualizations and interactive applications (e.g., Spotfire, Power BI, Plotly, Streamlit, Dash). • Core Concepts: • Deep understanding of statistics, probability, and ML algorithms (Regression, Classification, Clustering, Neural Networks). • Familiarity with Agile/Scrum methodologies. • Knowledge of Generative AI, LLMs, and data security best practices. • Typical Education: Bachelor’s degree in Data Science, Mathematics, Statistics, Analytics, Engineering, Geology, Computer Science, or related field. Master’s degree preferred. • Typical Experience: Minimum of 6 years related experience

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