Lead Data Scientist

SLB•Houston, TX

5d•Remote

About The Position

Responsible for providing technical leadership in the design, development, deployment, and lifecycle management of advanced data science, machine learning, and artificial intelligence solutions for industrial and production engineering applications. Focus on building scalable, production-grade analytical systems that support predictive maintenance, operational optimization, and engineering decision-making for asset-intensive environments, including pumps and electric submersible pump (ESP) systems. Design and implement end-to-end data science workflows encompassing data ingestion, feature engineering, model development, validation, deployment, monitoring, and continuous improvement. Require hands-on development of prognostics and health management (PHM) models using time-series, event-based, and operational datasets, as well as the implementation of ModelOps practices to ensure model reliability, version control, performance tracking, retraining, and governance in production environments. Lead the development and deployment of AI-enabled applications, including web-based analytical tools, dashboards, and decision-support systems, to deliver insights to technical and business stakeholders. Architect, build, and operate production-grade large language model (LLM) agents and LLM-based workflows, integrating them with enterprise data sources, analytical models, and software systems, and ensuring these solutions meet scalability, performance, and operational requirements. Leverage enterprise data science platforms, such as Dataiku or equivalent tools, to orchestrate analytics pipelines, manage model lifecycles, and enable collaboration across teams. Provide technical guidance and mentorship to other data scientists, contribute to architectural decisions for analytics and AI systems (including edge or near-edge deployments where applicable), and ensure compliance with internal software development, data governance, security, and operational standards.

Requirements

Master’s degree in Data Science, Computer Science, Computer and Information Science, Statistics, Engineering, Applied Mathematics, or a related STEM field, or foreign equivalent, plus 3 years of post-baccalaureate experience in the job offered or in data scientist, machine learning engineer, applied AI engineer, or related analytical job titles.
3 years of experience applying domain knowledge of oil and gas equipment and production systems to develop or deploy machine learning solutions using operational and sensor data for PHM, condition monitoring, or production optimization in production environments.
3 years of experience in reliability analytics using operational, sensor, and event-based data.
3 years of experience deploying and operating machine learning models in production systems, including integration and execution for edge or near-edge applications.
3 years of experience integrating machine learning, deep learning, LLM-based systems, and visualization tools with production engineering workflows.
3 years of experience in machine learning and deep learning model development for industrial assets, including predictive maintenance, anomaly detection, forecasting, and asset health monitoring in oil and gas production and engineering environments.
3 years of experience with enterprise data science and cloud platforms, including Dataiku, Microsoft Azure, and Google Cloud Platform (GCP), to build and manage data pipelines, ML workflows, and GenAI applications.
3 years of experience in Generative AI and RAG systems, including deploying and operating architectures combining LLMs with structured and unstructured data sources.
3 years of experience building interactive dashboards and analytical interfaces using frameworks such as React, Angular, Dash, or Streamlit.

Responsibilities

Provide technical leadership in the design, development, deployment, and lifecycle management of advanced data science, machine learning, and artificial intelligence solutions for industrial and production engineering applications.
Build scalable, production-grade analytical systems that support predictive maintenance, operational optimization, and engineering decision-making for asset-intensive environments.
Design and implement end-to-end data science workflows encompassing data ingestion, feature engineering, model development, validation, deployment, monitoring, and continuous improvement.
Develop prognostics and health management (PHM) models using time-series, event-based, and operational datasets.
Implement ModelOps practices to ensure model reliability, version control, performance tracking, retraining, and governance in production environments.
Lead the development and deployment of AI-enabled applications, including web-based analytical tools, dashboards, and decision-support systems.
Architect, build, and operate production-grade large language model (LLM) agents and LLM-based workflows, integrating them with enterprise data sources, analytical models, and software systems.
Leverage enterprise data science platforms, such as Dataiku or equivalent tools, to orchestrate analytics pipelines, manage model lifecycles, and enable collaboration across teams.
Provide technical guidance and mentorship to other data scientists.
Contribute to architectural decisions for analytics and AI systems (including edge or near-edge deployments where applicable).
Ensure compliance with internal software development, data governance, security, and operational standards.