Lead Data Scientist - Industrial AI & Prognostics at NOV · Kochi

Are you a motivated self-starter with the dedication to work independently and as part of a team, and an ability to multi-task? Do you have the flexibility and adaptability, to make decisions quickly? Do you thrive in dynamic environments with multiple changing priorities, where prioritization and time management are necessary tools?

We are seeking an experienced Senior Data Scientist with expertise in Predictive Maintenance, Prognostics and Health Management (PHM), and Industrial AI. You will develop advanced machine learning and AI solutions that improve equipment reliability, predict failures before they occur, optimize maintenance strategies, and enhance operational performance across NOV's global products and services.

Responsibilities

• Serve as an internal data science and AI expert, providing technical leadership and consulting to NOV business units, with a focus on Predictive Maintenance (PdM), Condition-Based Maintenance (CBM), and Prognostics & Health Management (PHM).

• Design, develop, validate, and deploy artificial intelligence and machine learning (AI/ML) and solutions for equipment health monitoring, anomaly detection, fault diagnosis, Remaining Useful Life (RUL) prediction, and maintenance optimization.

• Develop predictive analytics solutions using operational, engineering, and sensor data to improve asset reliability, operational efficiency, and business performance.

• Lead the end-to-end data science lifecycle, including problem definition, data exploration, feature engineering, model development, validation, deployment, and performance monitoring.

• Research and apply advanced AI technologies, including deep learning, computer vision, Generative AI (GenAI), and Large Language Models (LLMs), to solve other complex engineering challenges.

• Collaborate with engineers, software developers, product teams, and subject matter experts to translate business needs into scalable, production-ready AI solutions.

• Design scalable analytics pipelines and contribute to cloud-based data science and MLOps platforms that support enterprise AI applications.

• Communicate technical findings and recommendations to stakeholders and contribute to technical documentation, publications, patents, and innovation initiatives.

• Mentor junior team members and promote best practices in data science, machine learning, and AI across the organization.

Qualifications

You may be a good fit for our team, if you also meet the following requirements:

• Master's or Ph.D. in Computer Science, Data Science, Artificial Intelligence, Engineering, Applied Mathematics, Statistics, or a related STEM discipline. A Ph.D. is preferred.

• 7–10+ years of experience developing and deploying AI/ML solutions in industrial or engineering environments.

• Demonstrated experience in PdM/CBM/PHM, or asset reliability applications.

• Experience working with cross-functional engineering teams to deliver production-ready analytics solutions.

• Strong foundation in machine learning, deep learning, statistical modeling, and predictive analytics..

• Experience developing models for time-series analysis, anomaly detection, fault diagnosis, Remaining Useful Life (RUL) prediction, or failure forecasting.

• Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.

• Experience with computer vision, natural language processing (NLP), Generative AI, or Large Language Models (LLMs) is an advantage.

• Experience working with industrial equipment, sensor data, or operational systems in manufacturing, energy, oil & gas, or related industries.

• Knowledge of digital signal processing (DSP), condition monitoring, rotating machinery, hydraulic systems, or reliability engineering is highly desirable.

• Ability to interpret Process & Instrumentation Diagrams (P&IDs), engineering drawings, and system architectures to understand equipment configurations is a big plus.

• Familiarity with Digital Twins, physics-informed machine learning, or engineering simulation is a plus.

• Strong programming skills in Python and scientific computing libraries.

• Proficiency in leveraging AI coding assistants to accelerate software development, debugging, testing, and documentation while maintaining high standards of code quality.

• Experience with PySpark, SQL, and big data processing frameworks is an advantage.

• Familiarity with version control (Git) and software engineering best practices.

• Experience deploying machine learning models on cloud platforms such as AWS or Azure.

• Proficiency with Cloud, Github, and Docker is required, while Databricks, MLflow, CI/CD, or other MLOps technologies is preferred.

• Proven ability to translate research into practical engineering solutions.

• Experience authoring patents, invention disclosures, peer-reviewed publications, or technical papers is a big plus.

• Strong analytical and problem-solving skills with the ability to solve complex engineering challenges.

• Excellent communication and stakeholder management skills, with the ability to explain technical concepts to diverse audiences.

• Self-motivated, collaborative, and capable of leading multiple projects in a dynamic environment.

• Strong written and spoken English.