Role Overview
We 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 Responsibilities
1. Process Understanding & Model Validation
- Develop 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 Review
- Assess 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 Evaluation
- Review 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 Improvement
- Identify 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 Efficiency
- Challenge 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 Skills
Core Skills
- Advanced 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 approaches
- Deep understanding of optimization techniques, including:
- Linear Programming (LP)
- Constraint-based optimization models
- Objective function optimization for profitability
Modeling & Analytical Expertise
- Hands-on experience with models such as:
- LSTM (Long Short-Term Memory)
- Kalman Filters
- SARIMA
- Support Vector Regression (SVR)
- Ridge Regression
- Probabilistic and statistical models
- Strong knowledge of:
- Model validation techniques
- Overfitting prevention and proper train-test segregation
- Hyperparameter tuning and performance optimization
Mathematical & Analytical Skills
- Strong 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.