Lead the design, development, and deployment of advanced AI/ ML solutions for upstream oil & gas operations, including production optimization, predictive maintenance, anomaly detection, and operational efficiency improvements. Analyze production data, well performance metrics, sensor/ IoT data, and operational logs to identify patterns, forecast outcomes, and uncover opportunities for value creation. Collaborate with cross-functional teams — including production engineers, reservoir engineers, and field operations staff — to translate operational challenges into mathematical frameworks and data-driven solutions. Build and deploy end-to-end AI/ ML solutions in one or more upstream operations domains, applying best practices for data quality, explainability, and governance. Develop Gen. AI applications (chatbots, copilots, multi-agent workflows) and/or time series, optimization, or predictive analytics models tailored to upstream operational needs. Apply domain knowledge and physical principles to improve model accuracy and reliability in production and operations contexts. Ensure production readiness through ML - Ops practices (CI/ CD, M - Lflow, monitoring, cost optimization). Work closely with business stakeholders to understand operational problems, communicate analytical findings, and drive adoption of data science solutions. Contribute to the migration and enhancement of upstream digital analytics (UDA) tools, models, and workflows into the MODS framework. Mentor peers and contribute to internal AI capability building. About you Minimum Skills & Qualifications: 5 years of direct experience delivering production AI/ ML solutions, preferably in an upstream oil & gas or heavy industrial operations environment. Experience applying data science to upstream operations workflows, including production monitoring, well performance analysis, equipment reliability, process optimization, or operational planning. Demonstrated ability to work directly with business and operations teams to identify opportunities, scope analytical solutions, and deliver measurable impact. Strong foundations in statistics, probability, and algorithm design. Proficiency in Python and ML frameworks (Py. Torch, Tensor. Flow, scikit-learn); experience with Databricks/ Spark. Familiarity with Gen. AI frameworks (Lang. Chain, Promptflow) and/or optimization libraries. Experience with ML - Ops, model governance, and explainable AI techniques (e.g., SHAP, LIME). Excellent communication, collaboration, and problem-solving skills. Preferred Knowledge & Skills: Experience in the energy industry, specifically in upstream production operations, thermal/heavy oil operations (e.g., SAGD, CSS), or similar asset-intensive environments. Cloud platforms (Azure ML, Azure OpenAI, Databricks). Knowledge graphs, hybrid search/ RAG, and semantic technologies. Experience with time series analysis, anomaly detection, or predictive maintenance solutions for industrial operations. Familiarity with production data systems (e.g., SCADA, PI/ OS - Isoft, production databases). Agile development and software engineering best practices. Educational Background Recommended: Master's or PhD in Data Science, Computer Science, Engineering, Applied Math, or related field.