Data Science & Machine Learning Manager

Role OverviewWe are seeking a highly experienced Data Science & Machine Learning Manager to lead the review, validation, and optimization of advanced AI-driven solutions across complex operational and commercial value chains. This role requires a strong blend of technical expertise, business acumen, and domain understanding to enhance profitability and operational efficiency through data-driven decision-making.The ideal candidate will act as a Subject Matter Expert (SME), providing critical insights into AI/ML models, optimization strategies, and predictive systems while translating technical outputs into actionable business recommendations.Key Responsibilities1. Process Understanding & Model ValidationDevelop a deep understanding of gas supply and processing workflows across upstream and midstream operations.Review and validate physical simulation models, ensuring alignment with operational and process constraints.Evaluate production forecasting models, including component-level outputs (C1, C2, C3, C4, C5+).2. Feature Engineering ReviewAssess and validate feature engineering methodologies used in ML and deep learning pipelines.Ensure robustness, relevance, and completeness of input variables for predictive modeling.3. AI/ML Solution EvaluationReview and validate AI-driven solutions leveraging blended Machine Learning and Deep Learning approaches.Translate complex technical outputs into clear, business-friendly insights for stakeholders.4. Model Optimization & Performance ImprovementIdentify gaps in existing model designs and propose enhancements for improved accuracy and performance.Fine-tune model parameters, optimize feature importance, and prioritize constraints to achieve optimal outcomes.Recommend advanced optimization strategies for planning, scheduling, and commercial decision-making.5. Commercial Impact & Operational EfficiencyChallenge existing implementations to identify opportunities for improved efficiency and profitability.Drive data-driven strategies to optimize production outputs and business performance across the value chain.Required Technical SkillsCore SkillsAdvanced expertise in Python programming for data science and AI development.Strong experience in:Machine Learning (Regression, Classification, Clustering)Deep Learning (including sequence models, LSTM, attention mechanisms)Ensemble and hybrid modeling approachesDeep understanding of optimization techniques, including:Linear Programming (LP)Constraint-based optimization modelsObjective function optimization for profitabilityModeling & Analytical ExpertiseHands-on experience with models such as:LSTM (Long Short-Term Memory)Kalman FiltersSARIMASupport Vector Regression (SVR)Ridge RegressionProbabilistic and statistical modelsStrong knowledge of:Model validation techniquesOverfitting prevention and proper train-test segregationHyperparameter tuning and performance optimizationMathematical & Analytical SkillsStrong ability to interpret and apply mathematical equations in modeling.Advanced problem-solving and analytical thinking capabilities.Expertise in evaluating hybrid AI architectures to derive optimal solutions.