This platform is not an ERP system. It s a purpose-built digital layer that integrates data, processes, and resources across the entire manufacturing lifecycle from design and engineering to production and distribution.

This initiative is powered by NVIDIA technologies, including the Omniverse platform and AI computing capabilities, enabling immersive digital twins, accelerated simulation, and intelligent automation. You ll be part of a team that s not just building software but shaping the future of how manufacturing works through AI-driven, collaborative, and scalable digital solutions.

As part of this initiative, you ll contribute to:

- System Integration: Seamlessly connecting diverse manufacturing and supply systems, data sources, and workflows into a unified digital ecosystem.
- Data-Driven Decision Making: Harnessing real-time data collection, analysis, and visualization to deliver actionable insights and operational intelligence.
- Automation Optimization: Driving efficiency through intelligent scheduling, predictive maintenance, and quality control without replacing core transactional systems.
- Enhanced Collaboration: Enabling transparent communication and coordination across teams, functions, and geographies.

If youre passionate about digital platforms, industrial innovation, and working with cutting-edge technologies this is your opportunity to make a meaningful impact.

Key Responsibilities:

- C oach and mentor Junior Data Scientists in the creation, validation, and application of statistical, machine learning and LLM models as well as in implementing AI solutions
- Enhance team s creativity to solve business problems using machine learning techniques




- Evaluate and implement contemporary optimization algorithms: stochastic search, evolutionary/genetic, reinforcement, and multi-objective optimization.
- Lead expert-level validation of AI/ML models using industrial analytics metrics such as yield improvement, downtime reduction, and prediction accuracy.
- Establish standards for data/model lifecycle management, production monitoring, and feedback from physical operations.

Required Skills:

- Deep expertise in machine learning and deep learning techniques designs composable model services (ensembles, retrieval augmented generation, multimodal orchestration), establishes rigorous evaluation methods.
- Demonstrated experience in leveraging classical optimization (branch bound, heuristics, integer programming) techniques.
- Advanced Python knowledge to handle high-performance analytical workloads, simulation and deployment.
- Deep knowledge of manufacturing process analytics: multivariate analysis, defect analytics.
- Team/project leadership with publications or patents in industrial ML or optimization.
- Experience in building analytics on top of OpenUSD-powered asset graphs, integrating real/virtual process data for digital twins.

Nice to Have Skills:

- Hands-on with cloud-native deployments, industrial IoT/edge analytics, and data pipeline automation.
- Understanding of domains across plant/line telemetry, quality, maintenance, logistics, or scheduling can translate noisy signals into predictive and prescriptive use cases

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Lead Data Scientist (Bengaluru)
🏢 Caterpillar
📍 Bengaluru