Lead Data Scientist

Key Responsibilities:

• Coach 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 modern 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