Lead Data Scientist

SLB; Houston (TX); On-site; USD 140,000 - 210,000; Full time

Job summary

SLB in Houston seeks a Lead Data Scientist to provide technical leadership in the design, development, deployment, and lifecycle management of advanced data science, ML, and AI solutions for industrial and production engineering applications.

The role emphasizes production-grade analytics for predictive maintenance, asset optimization, and PHM, with hands-on model development, ModelOps, and LLM/GenAI workflows, using Dataiku, Azure, and GCP, and includes mentoring teammates and ensuring

Qualifications

• Master’s degree in Data Science, CS, or related STEM field plus 3 years of post-baccalaureate experience.
• Experience applying domain knowledge to develop/deploy ML solutions using sensor data for PHM, condition monitoring, or production optimization.
• Proven ability to deploy ML models in production and integrate with engineering workflows.

Responsibilities

• Provide technical leadership in design, development, deployment, and lifecycle management of advanced data science and AI solutions.
• Lead PHM model development using time-series and event-based data.
• Architect, build, and operate production-grade LLM agents and LLM-based workflows.

Skills

Data science leadership

PHM time-series analytics

ModelOps

LLM / GenAI

Dashboards & visualization

Team mentoring

Education

Master’s degree in STEM fields

Tools

Dataiku

Microsoft Azure

Google Cloud Platform (GCP)

React

Dash

Streamlit

Job description

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

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.

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.

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