PhysicsX · 6 days ago

Data Scientist

New York, United States

Full-time

Hybrid

Mid Level

Wonder how qualified you are to the job?

Maximize your interview chances

Artificial Intelligence (AI)Machine Learning

Insider Connection @PhysicsX

Discover valuable connections within the company who might provide insights and potential referrals, giving your job application an inside edge.

Responsibilities

Work closely with simulation engineers, machine learning engineers, and customers to develop an understanding of the physics and engineering challenges being solved

Build innovative models to predict the behavior of physical systems using machine learning and deep learning techniques

Own the delivery of data science workstreams

Design, build, and test data pipelines for machine learning that are reliable, scalable, and easily deployable

Discuss the results and implications of work with colleagues and customers

Contribute to internal R&D and product work

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Machine LearningDeep LearningProbabilistic MethodsProject ManagementPythonNumPySciPyPandasTensorFlowPyTorchAirflowSoftware EngineeringVersioningTestingDeploymentCloud EnvironmentProblem-SolvingCollaborationCommunicationSimulations for FEA/CFD

Required

Enthusiasm about using machine learning, especially deep-learning and/or probabilistic methods, for science and engineering

Ability to scope and effectively deliver projects

Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly

Excellent collaboration and communication skills - with teams and customers alike

Degree in computer science, machine learning, applied statistics, mathematics, physics, engineering or a related field

Helped build machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, TensorFlow, PyTorch, Airflow), especially including deep-learning applications

Software engineering concepts and best practices for collaborative programming (e.g., versioning, testing, deployment)

Working in a cloud environment with one of the major cloud providers