Lead Data Scientist

Full-time or part-time: Full-time

Job title: Lead Data Scientist

Job Location: 1430 Enclave Pkwy Houston TX 77077

Job Description:

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.

Minimum Education & Experience Requirements:

Masters 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.

Applicants must have 3 years of experience in the following: (1) 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; (2) reliability analytics using operational sensor and event-based data; (3) deploying and operating machine learning models in production systems including integration and execution for edge or near-edge applications; (4) integrating machine learning deep learning LLM-based systems and visualization tools with production engineering workflows; (5) 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; (6) 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; (7) Generative AI and RAG systems including deploying and operating architectures combining LLMs with structured and unstructured data sources; and (8) building interactive dashboards and analytical interfaces using frameworks such as React Angular Dash or Streamlit.

Telecommuting permitted less than 50% per week within the same geographic location as the assigned Schlumberger Office location.