BC Sector O and G-Data Scientist-Senior Manager

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.

EY GDS | PRINCIPAL DATA SCIENTIST Oil & Gas | Senior Manager Job Title     Principal Data Scientist – Senior Manager       Experience Level        15+ Years   Job Description At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. We’re counting on your unique voice and perspective to help EY become even better. Join us and help build a better working world. We are seeking a hands-on Principal Data Scientist to solve complex modelling problems across upstream and downstream Oil & Gas. You will formulate operational challenges, establish defensible scientific approaches, develop critical modelling components, and define the evidence required for reliable deployment across wells, fields, equipment, process units, and sites.   Role Details Key responsibilities include : Scientific formulation and advanced modelling

Formulate operational problems as predictive, diagnostic, causal, or prescriptive tasks, defining targets, objective functions, constraints, decision variables, acceptance criteria, and evaluation methods. Assess data sufficiency, measurement quality, sampling bias, confounding, identifiability, and operating coverage to determine whether available evidence supports the intended outcome. Select statistical, machine learning, simulation, or hybrid approaches suited to noisy measurements, heterogeneous assets, sparse labels, rare events, censored histories, and distribution shifts. Apply probabilistic or hierarchical modelling, uncertainty quantification, survival analysis, semi-supervised learning, transfer learning, or domain adaptation where appropriate. Develop methods for multivariate forecasting, state estimation, change detection, degradation modelling, and industrial time-series analysis while distinguishing process dynamics from measurement artefacts. Combine learned models with engineering equations, mass and energy balances, simulation outputs, or physical constraints, evaluating accuracy, plausibility, extrapolation behaviour, and uncertainty propagation. Connect predictions with constrained optimization, assessing feasibility, sensitivity, robustness, and decision quality under uncertainty; design experiments or causal analyses when observational evidence is insufficient.

Upstream applications

Define modelling approaches for production forecasting, well surveillance, artificial-lift performance, drilling analytics, and production-system optimization. Separate the effects of reservoir behaviour, operating constraints, equipment condition, interventions, formation changes, and operating practices when interpreting performance. Collaborate with reservoir engineers and geoscientists on property prediction, uncertainty analysis, and simulation-assisted modelling. Determine whether models can transfer between wells or fields and define the data, recalibration, adaptation, and validation required.

Downstream applications

Define modelling approaches for product-quality prediction, yield modelling, equipment degradation, blending, and process or energy optimization. Account for feedstock composition, throughput, catalyst age, equipment condition, process configuration, and control-system behaviour. Combine data-driven methods with engineering relationships, process simulations, and mass and energy balances. Connect predictive models with operating, planning, blending, or optimization workflows within defined physical and operational constraints, validating across feedstock