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PhysicsX

Physics AI platform for industrial engineering

Data Scientist

Applied Machine LearningUpdated on Nov 14
Full-timeNew York, NY, USA

About the Job

Requirements

  • At least one year of industry experience after completing a Master's or PhD, in a commercial, non-research environment.
  • Strong foundations in data-driven modeling and deep learning techniques, with hands-on experience in probabilistic methods and predictive modeling.
  • Expertise in Python and proficiency with NumPy, SciPy, Pandas, TensorFlow, and PyTorch.
  • Ability to deploy scalable, production-ready models and data pipelines.
  • Ability to understand advanced engineering concepts across multiple industries and work directly with customers to turn AI models into practical engineering tools.

Responsibilities

  • Work with Simulation Engineers, Machine Learning Engineers, and customers to understand and define engineering and physics challenges.
  • Pre-process and analyze data for predictive modeling and prepare foundations for machine-learning algorithms.
  • Develop and use deep-learning models with optimization methods to predict and control physical-system behavior.
  • Take responsibility for the quality, accuracy, and impact of work.
  • Design, build, and test reliable, scalable data pipelines that can be deployed in production environments.
  • Work with simulation engineers to integrate data-science models with simulations.
  • Contribute to internal research and development and product development by refining models and identifying new application areas.
  • Communicate and present with technical teams and customers, onboard users, and co-develop solutions with customers.
  • Travel to customer sites in North America, Europe, Asia, and Oceania for an average of 3–4 weeks per quarter to collaborate on-site and build solutions.

About the Company

PhysicsX

Physics AI platform for industrial engineering

PhysicsX uses physics-based artificial intelligence for industrial engineering, combining machine learning with physics simulations to shorten design and operation cycles for aerospace, energy, and automotive sectors. Its platform replaces multi-day simulations with near-real-time models that predict performance, helping teams test ideas faster while reducing emissions and costs. It differs from competitors by delivering physics-driven AI with fast, scalable simulations tailored to large, complex industrial systems, supported by a slate of major investors and industrial partners. The company aims to speed up product development and drive broad adoption of physics AI across industrial sectors.

Company size
201-500
Company stage
Series C
Total funding
$487M
Headquarters
London, United Kingdom
Founded
2019

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