Location: Software Technology Innovation Center (STIC), Menlo Park, CA, USA

Job Summary

We are seeking scientists and engineers with a strong fundamental understanding of various modern data-driven methods to address highly challenging scientific & engineering problems in applications of Artificial Intelligence, Optimization, and Data Science to industrial problems in the Energy sector.

The candidate is responsible for conducting undirected technology development and tackling open-ended AI/ML problems and questions. Drawing on an advanced degree in a quantitative field such as computer science, physics, statistics, engineering, or applied/computational mathematics, the ideal candidates demonstrate the knowledge to invent new algorithms to solve data problems.

The candidate participates in data science, artificial intelligence, machine learning, and industrial analytics, with specific emphasis on modern deep learning methods, foundation models, and generative AI solutions.

Work with product champions, product managers, and designs to engineer the appropriate computer system solution for SLB.
Communicate sophisticated ML concepts to management, clients, and the business community.
Research and assess next-generation technologies for inference, predictive modeling, general-purpose data-driven modeling, and optimization of complex systems. Demonstrate advanced working knowledge and experience with data analytics, machine learning algorithms, and optimization methods.
Generate innovative ideas, establish new technology development directions, and shape and execute technical projects.
Maintain state-of-the-art knowledge and contribute to technical discussions and reviews as an expert in related areas of responsibility.
Communicate ideas, plans, and results effectively via oral and written reports. Works effectively with peers, management, operations groups, and outside organizations.
May participate in the relevant technical reviews and audits of the projects.
Review, mentor, and coach junior team members while defining and promoting the use of standards, best practices, and lessons learned.
Work across multiple cross-functional teams in high visibility roles to prototype end-to-end data solutions.

Relationships: Reports to Software Project Manager, Engineering Manager, or Team Lead

Responsibilities:

Work on developing and tuning robust machine learning and data-science models to previously unsolved domain problems.
Explore machine learning engineering, AI and Data Science new technologies in a continues basis and assess their suitability for their use in SLBs stack.
Identify and communicate to the community of stakeholders about upcoming technologies.
Ensure that data science code is maintainable, scalable, and deployable in modern computational stack.
Bring the best software development practices to the data science team and help them accelerate their work.
Together with the DevOps team, choose the best operational architectures. Look for performance improvement and decide which machine learning technologies will be used in the production environment.

Minimum Technical and Experience Requirements:

1-5 years of experience in applied technology development, applied research of deep learning in physical-systems, or a combination of both.
Experience in data-driven modeling using modern data science techniques from statistics and machine learning.
Ability to prototype cutting-edge research models (foundation models and GenAI) and make an assessment of their applicability to a variety of SLB domain challenges.
Hands-on experience in modern Machine learning frameworks such as TensorFlow and PyTorch, with a strong emphasis on full computer system design.
Expertise with one or more of the AI, Data Science or Machine Learning solution suite from major cloud providers.
Computer programming skills for early-stage prototyping and development towards the production of suitable software to be deployed in modern system stack (cloud, containers, etc.)
Advanced ability to work with data-driven models, including parameter tuning, feature engineering and other associated tasks. Experience on robust model development that honors business and engineering requirements in terms of decision making and decision support.
PhD or Masters degree in a Scientific or Engineering discipline with strong emphasis on the mathematics fundamentals of Data Science, Artificial Intelligence. Engineering training at the Undergraduate level is a plus.
Team-oriented behaviors and skills and comfortable taking technology and scientific risks to explore solutions and approaches outside traditional viewpoints and guide others towards such vision.
Very good written and verbal communication skills. Strong ability to communicate advanced data science and AI concepts to others with a background in traditional engineering disciplines and/or pure business background.
Proficiency in programming languages such as Python, R, or others related to modern data-driven products for IoT/Edge

The compensation and benefits for this role are listed in compliance with applicable law. We are committed to offering fair and competitive pay aligned with each candidate’s skills, experience, and qualifications, within the designated salary range for the role. Please note that the listed compensation and benefits apply only to successful candidates hired onto a local United States payroll. The anticipated annual salary range for this position is $140,000 - $212,000.

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