Principal Data Scientist - Oil & Gas (Senior Manager)

About the role

EY is seeking a hands-on Principal Data Scientist (Senior Manager) to solve complex modelling problems across upstream and downstream Oil & Gas. The role formulates operational challenges, establishes defensible scientific approaches, develops critical modelling components and defines the evidence needed for reliable deployment across wells, fields, equipment, process units and sites.

Key responsibilities

• Formulate operational problems as predictive, diagnostic, causal or prescriptive tasks, assess data sufficiency and measurement quality, and select statistical, machine learning, simulation or hybrid approaches.
• Apply probabilistic or hierarchical modelling, uncertainty quantification, survival analysis, semi-supervised learning, transfer learning and domain adaptation.
• Develop methods for multivariate forecasting, state estimation, change detection, degradation modelling and industrial time-series analysis.
• Combine learned models with engineering equations, mass and energy balances and simulation outputs, and connect predictions with constrained optimization.
• Define modelling approaches for production forecasting, well surveillance, artificial lift, drilling analytics and product quality, yield and blending.
• Set standards for baselining, benchmarking, calibration, sensitivity analysis and out-of-distribution evaluation, and mentor Lead Data Scientists.

Required profile

• Degree in a relevant engineering, scientific or quantitative discipline.
• Substantial hands-on experience developing and deploying Oil & Gas analytical solutions, with deep expertise in at least one upstream or downstream area.
• Experience integrating engineering knowledge into model formulation and validation, and conducting technical reviews and mentoring data science teams.
• Ability to defend technical decisions with quantitative evidence and communicate assumptions and trade-offs clearly.
• Good to have: delivery experience across both upstream and downstream, a master's degree or PhD, and expertise in system identification, robust optimization, physics-informed learning or simulation surrogates.

Required skills

• Advanced expertise in at least one applied modelling specialization.
• Strong foundations in statistics, machine learning, mathematical modelling, industrial time series, scientific computing and experimental design.
• Strong Python and SQL skills, including scientific prototyping and review of production code.
• Good to have: distributed scientific computing, cloud ML platforms and production MLOps.

What we offer

• A hands-on technical role with scientific leadership and the chance to set reusable methods and standards.
• A collaborative environment spanning domain engineering, data science, ML engineering, product and global EY teams.
• Access to EY's global network, technology ecosystem and learning opportunities.