Data Scientist, Completions Analytics

Job description

About the role We are seeking a Senior Data Scientist to support the research, development, and deployment of advanced analytics solutions for hydraulic fracturing, completions, and production operations. This role focuses on applying data analysis, statistical modeling, physics-informed approaches, and software development to solve complex operational challenges. The ideal candidate combines strong Python programming skills with a deep understanding of oilfield operations, particularly completions and hydraulic fracturing. You will work closely with engineers, domain experts, and software developers to transform operational data into scalable tools, workflows, and decision-support applications that improve efficiency, reliability, and performance across customer operations. What you'll do Analytics & Applied Research Analyze large-scale operational datasets from completions, hydraulic fracturing, and production operations Develop statistical, physics-based, empirical, and data-driven models to improve operational understanding and decision making Conduct applied research focused on frac performance, pumping efficiency, equipment reliability, pressure analysis, and operational optimization Design and execute studies to identify key drivers of operational performance Develop algorithms and workflows for anomaly detection, forecasting, diagnostics, and optimization Translate engineering problems into analytical solutions that provide measurable business value Validate models and recommendations using field data and operational feedback Software Development & Data Engineering Design, develop, and maintain production-quality Python applications and analytics tools Build scalable data processing pipelines for structured and time-series datasets Develop reusable software components, libraries, and APIs supporting analytics workflows Write clean, maintainable, and well-tested code following software engineering best practices Participate in code reviews and contribute to shared codebases Collaborate with software engineers to integrate analytical solutions into customer-facing products Collaboration & Business Impact Work closely with completions engineers, product managers, and software teams to define requirements and deliver solutions Communicate analytical findings clearly to technical and non-technical stakeholders Support customers and internal teams in understanding operational trends and performance drivers Identify opportunities to improve operational efficiency, reduce costs, and increase asset performance Document methodologies, assumptions, and analytical workflows Qualifications Education & Experience Master's or PhD in Petroleum Engineering, Data Science, Statistics, Applied Mathematics, Engineering, Computer Science, or a related quantitative field 5+ years of experience in data science, analytics, engineering research, or software development Experience working with oil and gas operational data Strong preference for candidates with completions, hydraulic fracturing, stimulation, or production optimization experience Experience delivering analytical solutions that drive measurable operational improvements Technical Skills Strong Python programming skills with experience developing production-quality software Experience with scientific computing and data analysis libraries such as Pandas, NumPy, SciPy, and scikit-learn Strong SQL and database experience Experience working with time-series data and large operational datasets Knowledge of statistical analysis, predictive modeling, and experimental design Experience developing optimization, forecasting, or diagnostic workflows Familiarity with software development best practices, version control, testing, and code reviews Oil & Gas Domain Expertise Strong understanding of hydraulic fracturing and completions operations Knowledge of frac equipment, pumping operations, pressure analysis, treatment design, and operational KPIs Experience analyzing frac, wireline, production, or well-performance datasets Ability to identify operational anomalies, inefficiencies, and root causes from field data Understanding of operational workflows and engineering decision-making processes Preferred Qualifications Experience with real-time operational data systems Familiarity with cloud environments and data platforms Experience with machine learning applications for forecasting, classification, or anomaly detection Familiarity with geoscience, production, or reservoir engineering datasets Experience publishing technical papers, patents, or industry presentations Professional Skills Strong analytical thinking and problem-solving abilities Excellent written and verbal communication skills Ability to work independently and manage multiple projects simultaneously Effective collaboration within multidisciplinary teams Ability to balance technical rigor with practical business needs Strong organizational and documentation skills About Corva Corva has built a first-of-its-kind energy app store on a bedrock of best-in-class technologies, data pipelines, and a secure and scalable architecture. Our energy solutions solve today's toughest well delivery challenges, from well design through drillout. The ever-evolving platform is not only future-proof for digitizing operations but is your toolkit to accelerate sustainability and energy transition goals. Our platform is built for speed and reliability and delivers unmatched features and capabilities. Corva is powering worldwide innovation by driving efficiency, productivity, and profitability with our innovative energy solutions. Mission Corva’s mission is to accelerate the future of energy. Values Boldness : Corvanauts have the confidence and courage to question status quo for the products we make and the relationships we cultivate. Own End-to-End : We take ownership of what we start and see it through to completion through trust and dependability. Transparency : It's crucial to be open and honest and consistent with updates and data flow with customers and colleagues. We value the free-flowing of information and data to make better decisions. Bias Action : Corvanauts don't sit still - our default mode is taking action! We make progress through high-quality iterations. Failure is built into the process and success is defined by the number of shots on goal.