Lead Data Scientist - Industrial AI & Prognostics

Job Overview

We are looking for a seasoned Senior Data Scientist specialized in Predictive Maintenance, Prognostics, Health Management, and Industrial AI to develop cutting-edge machine learning and AI systems. These solutions will advance equipment reliability, proactively predict failures, optimize maintenance approaches, and boost performance across NOV’s global product and service lines.

Key Responsibilities

• Act as the in-house expert in data science and AI, offering technical leadership and consulting services focused on Predictive Maintenance, Condition-Based Maintenance, and Prognostics & Health Management.
• Design, build, validate, and operationalize AI/ML models for monitoring equipment health, detecting anomalies, diagnosing faults, predicting Remaining Useful Life (RUL), and optimizing maintenance schedules.
• Create predictive analytics frameworks using operational, engineering, and sensor datasets to enhance asset reliability and overall business outcomes.
• Oversee the entire data science process from problem identification through data exploration, feature creation, modeling, evaluation, deployment, and ongoing monitoring.
• Investigate and apply advanced AI methodologies including deep learning, computer vision, Generative AI, and Large Language Models to tackle complex engineering problems.
• Collaborate closely with cross-disciplinary teams including engineers, developers, and product managers to convert business requirements into scalable, robust AI-driven applications.
• Architect scalable data pipelines and contribute to cloud-based data platforms supporting enterprise-wide AI deployments and MLOps.
• Deliver clear technical communications to stakeholders and contribute to documentation, patents, publications, and innovation projects.
• Mentor junior analytics staff and champion data science best practices throughout the organization.

Qualifications

• Master’s or Ph.D. degree in Computer Science, Data Science, AI, Engineering, Applied Math, Statistics, or related STEM fields, with preference for Ph.D. holders.
• 7–10+ years of industry experience developing and deploying AI/ML in engineering or industrial contexts.
• Proven expertise in Predictive Maintenance, Condition-Based Maintenance, Prognostics, or asset reliability applications.
• Experience collaborating with engineering teams to deliver production-level analytics solutions.
• Strong foundations in machine learning, deep learning, statistical methods, and predictive analytics, including time-series modeling, anomaly detection, fault diagnosis, and failure forecasting.
• Hands-on experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.
• Knowledge of computer vision, NLP, Generative AI, or Large Language Models is a plus.
• Experience handling industrial equipment data from manufacturing, energy, oil & gas, or similar sectors.
• Familiarity with digital signal processing, condition monitoring of rotating machinery, hydraulics, and reliability engineering is highly advantageous.
• Ability to interpret engineering diagrams (P&IDs), drawings, and system architectures to comprehend equipment layouts is beneficial.
• Understanding of Digital Twins, physics-informed ML, or engineering simulations is favorable.
• Advanced proficiency in Python programming and scientific computing libraries.
• Skilled use of AI coding assistants to expedite development tasks while upholding code quality.
• Experience with PySpark, SQL, and large-scale data processing frameworks is an asset.
• Competence with version control systems (Git) and software engineering best practices.
• Experience deploying ML models on cloud platforms such as AWS or Azure.
• Proficiency in Cloud services, GitHub, and Docker is required; knowledge of Databricks, MLflow, CI/CD, and other MLOps technologies is preferred.
• Capability to convert research insights into practical, operational engineering solutions.
• Contributions to patents, invention disclosures, scholarly articles, or technical publications are a strong advantage.
• Excellent analytical and problem-solving abilities to address complex engineering challenges.
• Strong communication and stakeholder management skills, comfortable explaining sophisticated concepts to varied audiences.
• Self-driven, collaborative, and adept at managing concurrent projects in a fast-paced setting.
• Fluent in written and spoken English.