PhysicsX Logo

PhysicsX

Data Scientist

Posted 8 Days Ago
In-Office
New York, NY, USA
120K-240K Annually
Junior
In-Office
New York, NY, USA
120K-240K Annually
Junior
Develops predictive and deep learning models for engineering and physical systems, including data preprocessing, probabilistic modeling, optimization, and scalable production data pipelines. Collaborates with simulation engineers and customers to integrate AI models into simulations, support product development, and deliver practical solutions. The role includes customer presentations, onboarding, on-site collaboration, and travel to international customer sites.
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About us
Re-architecting Engineering for the Age of Intelligence

PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area.
Who We're Looking For
As a Data Scientist in Delivery, you are a problem solver and builder who is passionate about creating practical solutions that enable customers to make better engineering decisions. You are someone who can grasp advanced engineering concepts across multiple industries, and you excel at working directly with customers (and often side-by-side with them on-site) to transform cutting edge AI models into tools that are useful and used.
 
You’ve worked on difficult problems that require strong foundations in data driven modelling and deep learning techniques, with hands-on experience in probabilistic methods and predictive modelling. Expertise in python, along with proficiency in libraries like NumPy, SciPy, Pandas, TensorFlow and PyTorch, is essential, with the ability to deploy scalable, production-ready models and data pipelines.
 
With at least 1 year industry experience (post Masters or PhD) in a commercial, non-research environment, you’re ready to hit the ground running. You’re truly excited about growing your technical expertise and are naturally inclined to take ownership of data science work streams, continuously improving the systems and solutions you work on to ensure they are practical, impactful and meet the evolving needs of our customers.
 
Note: This position may require access to information protected under U.S. export control laws and regulations, including the Export Administration Regulations (EAR) and the International Traffic in Arms Regulations (ITAR).  Please note that any offer for employment may be conditioned on authorization to receive software or technology controlled under these U.S. export control laws and regulations without sponsorship for an export license.
This Role
In this role, you’ll work closely with our Simulation Engineers, Machine Learning Engineers, and customers to understand and define the engineering and physics challenges we are solving.
You’ll build the foundations for successful, impactful solutions by:
 
  • Pre-processing and analyzing data to prepare it for use in predictive modelling, building the foundation for machine learning algorithms to be developed.
  • Developing and utilizing innovative deep learning models in combination with state-of-the-art optimization methods to predict and control the behaviour of physical systems.
  • Taking full responsibility for the quality, accuracy and impact of your work.
  • Designing, building and testing data pipelines that are reliable, scalable and easily deployable in production environments.
  • Working closely with simulation engineers to ensure seamless integration of data science models with simulations.
  • Contributing to internal R&D and product development, helping to refine models and identify new areas of application.
  • Engaging in open communication and presentation with both technical teams and customers, helping onboard users and co-develop with customers.
You'll also have the opportunity to travel to customer sites in North America, Europe, Asia, Oceania, for an average of 3-4 weeks per quarter, where you'll collaborate closely with customers to build solutions on-site.
 
As the role evolves, there are exciting opportunities for growth as an individual contributor or a technical lead, especially if you’re driven by taking ownership of more complex projects and leading the direction of future solutions.
Please note, this role is based in Manhattan, NYC, working 2-3 days per week in our office.
 
Our delivery teams drive innovation to turn AI models into practical solutions - read our blog to learn more about how you’ll contribute to this exciting journey!

What we offer

Build what actually matters

Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind.

Learn alongside exceptional people

Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home.

Influence over hierarchy

We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected.

Sustainable pace, long-term ambition

Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our New York office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person.

And it doesn’t stop there …

🚀 Equity options - share meaningfully in the company you’re helping to build.

💰 5% contribution to 401(k) - build long-term security with a strong retirement plan.

🍽️ Free team lunch 1x/week - good food, great company, and space to connect.

🏥 Private health insurance – comprehensive cover for you, offering total peace of mind.

👶 Enhanced parental leave – 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most.

☀️ 20 days of Annual Leave (+ Public Holidays) - because taking time to rest matters.

📈 Personal development – dedicated support for learning, development, and leveling up over time.

💪 Gympass / Wellhub (subsidized) – for you and up to 3 family members, supporting both physical and mental wellbeing.

💳 Flexible Spending Account (FSA) – set aside pre-tax dollars for eligible healthcare expenses.

🔎 Watch this space, we’re continuing to build this as we grow…

Salary Range 
 
$120,000 - 240,000 depending on experience 
Seniority will be assessed throughout our interview process 
 
We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics. 
 
We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application. 
 

PhysicsX New York, New York, USA Office

2nd Floor 154 W 14th Street, New York, NY, United States, 10011

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