Data Science & Machine Learning Manager
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Key skills for this role
About the Role
Lead the review, validation, and optimization of AI/ML solutions for gas supply and processing operations. Act as a subject matter expert, evaluating models, feature engineering, and optimization strategies to enhance profitability and operational efficiency.
Key Skills for This Role
Responsibilities
- Develop deep understanding of gas supply and processing workflows across upstream and midstream operations
- Review and validate physical simulation models and production forecasting models
- Assess and validate feature engineering methodologies used in ML and deep learning pipelines
- Review and validate AI driven solutions leveraging blended ML and Deep Learning approaches
- Translate complex technical outputs into business friendly insights for stakeholders
- Identify gaps in existing model designs and propose enhancements for improved accuracy
- Fine tune model parameters, optimize feature importance, and prioritize constraints
- Recommend advanced optimization strategies for planning, scheduling, and commercial decision making
- Challenge existing implementations to identify opportunities for improved efficiency and profitability
Requirements
- Advanced expertise in Python programming for data science and AI development
- Strong experience in Machine Learning (Regression, Classification, Clustering) and Deep Learning (LSTM, attention mechanisms)
- Deep understanding of optimization techniques including Linear Programming and constraint based optimization
- Hands on experience with models such as LSTM, Kalman Filters, SARIMA, SVR, Ridge Regression
- Strong knowledge of model validation, overfitting prevention, and hyperparameter tuning
- Ability to interpret and apply mathematical equations in modeling
Full Job Posting
Role Overview
- Seeking a highly experienced Data Science & Machine Learning Manager to lead review, validation, and optimization of advanced AI driven solutions across complex operational and commercial value chains.
- Act as a Subject Matter Expert (SME), providing critical insights into AI/ML models, optimization strategies, and predictive systems.
Key Responsibilities
- Develop 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.
- Assess and validate feature engineering methodologies used in ML and deep learning pipelines.
- 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.
- Identify gaps in existing model designs and propose enhancements for improved accuracy and performance.
- Fine tune model parameters, optimize feature importance, and prioritize constraints.
- Recommend advanced optimization strategies for planning, scheduling, and commercial decision making.
- Challenge existing implementations to identify opportunities for improved efficiency and profitability.
Required Technical 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, constraint based optimization, objective function optimization for profitability.
- Hands on experience with models such as LSTM, Kalman Filters, SARIMA, Support Vector Regression, Ridge Regression, probabilistic and statistical models.
- Strong knowledge of model validation techniques, overfitting prevention, proper train test segregation, hyperparameter tuning.
- 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.
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