Senior Data Scientist

Required Skills & Qualifications

• Bachelor’s or master’s degree in operations research, Computer Science, Data Science, or a related quantitative discipline, with a strong foundation in optimization, simulation, and algorithmic problem‑solving.
• Minimum of 3 years of hands‑on experience in 3D modelling and simulation using platforms such as NVIDIA Omniverse or equivalent tools.
• Strong proficiency in Python, R, SQL, and modern data engineering practices.
• Practical experience with machine learning frameworks such as TensorFlow and PyTorch, as well as simulation and modeling tools.
• Solid understanding of semiconductor manufacturing processes and fab operations.
• Familiarity with IoT data pipelines, MES systems, and cloud platforms such as AWS and Azure.

Key Responsibilities

• Collaborate with a global, cross‑functional team of data scientists, data engineers, solution architects, external partners, and stakeholders from construction, facilities, and planning teams to develop highly accurate digital twins of semiconductor fabs.
• Enable advanced digital twin capabilities, including photorealistic visualization and physics‑based simulation, to support planning and operational decision‑making.
• Design, build, and maintain data‑driven models that represent semiconductor fab processes,equipment behavior, and sub‑fab utility systems.
• Integrate IoT sensor data, Manufacturing Execution System (MES) data, and facility data into the digital twin platform to ensure real‑time and historical insight.
• Develop predictive analytics and machine learning models for predictive maintenance, throughput optimization, energy efficiency, and yield improvement.
• Apply statistical and ML techniques to forecast equipment failures, cycle time variability, and potential yield impacts.
• Leverage simulation tools such as NVIDIA Omniverse to conduct what‑if analysis for layout planning, capacity expansion, and process optimization.
• Optimize fab workflows to improve cost efficiency, energy consumption, and overall productivity.
• Design and deliver dashboards for real‑time monitoring of key fab KPIs, including OEE, WIP, and energy usage.
• Partner with engineering teams to visualize fab layouts, system states, and operational scenarios within immersive 3D environments.
• Support and contribute to cross‑functional initiatives related to AI, automation, Industry 4.0, and smart manufacturing solutions.

To Apply:

Interested applicants please send your resume in MS Words format to [email protected] and attention to Pooja (R1551322)

Short listed candidates will be notified

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EA Registration Number: R1551322

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