Advanced Data Scientist

Data Scientist - Advanced Analytics

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• Spearhead advanced analytics initiatives to optimize exploration, production, and operational efficiency.

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• Design and implement end-to-end data science projects from defining business problems to deploying advanced machine learning models at scale.
• Architect and build predictive models, statistical algorithms, and machine learning systems to solve complex business challenges and enhance decision-making.
• Provide technical leadership and mentorship to junior data scientists, promoting best practices and guiding technical development across the team.
• Innovate new methodologies in machine learning and AI, staying at the forefront of emerging tools, techniques, and industry trends.
• Lead cross-functional teams, ensuring alignment and seamless integration between data science, engineering, and business teams.
• Implement robust data governance, validation, and quality assurance processes to ensure the integrity and reliability of data science outputs.
• Partner with data engineering and IT teams to build scalable, automated data pipelines that support data science initiatives.
• Present complex data insights and machine learning results to non-technical stakeholders, ensuring clarity and business relevance.
• Drive the design and execution of experiments, A/B tests, and statistical analyses to measure and optimize the impact of data-driven decisions.
• Ensure compliance with regulatory requirements, data security, and governance protocols when building scalable data science solutions.
• Understand client needs and provide tailored, strategic solutions that align with business objectives.
• Build strong relationships by clearly communicating technical concepts and managing expectations.
• Actively participate in recruiting top technical talent for the team.
• Collaborate with the sales team in presales activities, identifying client needs, providing technical expertise, and crafting data-driven solutions to meet business requirements.
• Define and implement data strategies to support exploration, drilling, and production business goals.
• Oversee data collection, cleaning, and integration from diverse sources (e.g., seismic, production logs, IoT sensors, SCADA systems).
• Ensure data accuracy, consistency, and security while adhering to industry compliance standards.
• Design and implement advanced machine learning models (e.g., predictive maintenance, reservoir simulations, production optimization).
• Develop algorithms for seismic data interpretation, reservoir characterization, and well-performance forecasting.
• Optimize workflows using natural language processing (NLP) for unstructured data, such as drilling reports and maintenance logs.
• Conduct exploratory data analysis (EDA) to identify trends, anomalies, and optimization opportunities.
• Utilize geospatial analysis and geostatistical techniques to interpret geological and geophysical data.
• Implement real-time data analytics for drilling, well monitoring, and production enhancement.
• Lead the adoption of cloud-based data platforms (e.g., Azure, AWS, Google Cloud) for scalable computation.
• Drive automation of repetitive tasks using advanced scripting and machine learning pipelines.
• Mentor junior data scientists and engineers, fostering a culture of innovation and excellence.
• Collaborate with engineers, geophysicists, and reservoir managers to translate business challenges into data science solutions.
• Communicate technical findings to non-technical stakeholders through visualizations and reports.
• Develop optimization models for energy efficiency, cost reduction, and supply chain logistics.
• Enhance drilling accuracy and reduce downtime through predictive analytics for equipment maintenance.
• Implement risk assessment models to improve safety and compliance standards.
• Expertise in Python, R, MATLAB, and SQL for statistical modeling and data analysis.
• Proficient in machine learning libraries (e.g., TensorFlow, PyTorch, Scikit-learn) and big data tools (e.g., Hadoop, Spark).
• Familiarity with domain-specific software like Petrel, Schlumberger, or Halliburton's Decision Space.