Data Science Supervisor

Job Title: Data Science Supervisor

Position Summary

The Chord Energy Data Science Supervisor is a working manager that will lead a small team of data scientists/ML engineers while staying hands-on building models yourself. The team develops, deploys, and monitors machine-learning solutions for subsurface (geology, geophysics, reservoir), operations (production, D&C, reliability), and back-office (supply chain, HSE, finance) use cases that drive measurable business value. This role presents an exciting opportunity for a seasoned data science supervisor to contribute to the advancement of Chord's data science transformation, leveraging expertise in data analytics to drive innovation and optimize operational outcomes. This position is located in downtown Houston, TX. Hybrid work schedule is an option for remote work on Mondays and Fridays. Level and salary commensurate with experience.

Essential Job Functions

The primary responsibility involves leading a small team and prioritizing and manage the data-science backlog; translate business objectives into model roadmaps and delivery plans across subsurface, operational, and enterprise domains. Lead the full model lifecycle—problem framing, experiment design, feature engineering, training/validation, deployment, and post-production monitoring—with strong MLOps practices. Be hands-on: write Python/SQL, build and review notebooks/pipelines, perform code reviews, and mentor the team on statistical rigor and software engineering. Maintain and evolve the team toolset (Azure ML, OpenAI/Azure AI Foundry, Snowflake/feature store, MLflow), ensuring reliability, security, cost control, and access governance.

Minimum Qualifications

• Bachelor’s degree in a quantitative field
• 7+ years in data science/ML (including time-series, geospatial, and predictive modeling)
• Proficiency in Python (pandas, scikit-learn) and SQL; experience with Azure ML/MLflow and deploying models to production.
• Domain familiarity with upstream oil & gas workflows (subsurface and operations) and the ability to translate expert knowledge into features and experiments.
• Strong coaching, prioritization, and stakeholder-management skills; able to convert business problems into robust, scalable model solutions and communicate outcomes clearly.
• Strong interpersonal and collaborative skills
• Demonstrated ability to work in a team-oriented environment
• Excellent presentation skills
• Ability to work in a fast-paced and fluid environment; flexible with the demands of a growing company
• Ability to meet deadlines
• Physical Requirements and Working Conditions: Must possess mobility to work in a standard office setting and to use standard office equipment, including a computer, stamina to maintain attention to detail despite interruptions, strength to lift and carry files weighing up to 10 pounds; vision to read printed materials and a computer screen, and hearing and speech to communicate in person and over the telephone

The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Preferred Qualifications

• Master’s degree or PHD or certification in Data Science or Data Analytics
• Familiarity with additional programming languages (SQL, R, C++, etc.)
• Previous Oil and Gas operator experience
• Exposure to Petroleum Engineering and Geoscience workflows
• Exposure to the energy sector, particularly unconventional exploitation programs

This role presents an exciting opportunity for a Data Science Supervisor to contribute to the advancement of Chord's data science transformation, leveraging expertise in data analytics to drive innovation and optimize operational outcomes.

EEO Statement:

Chord Energy does not discriminate in employment on the basis of race, color, religion, sex (including pregnancy and gender identity), national origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non-merit factor.