
Data Scientist (Delivery)
Posted yesterday
The role in short
Build and deploy production-ready deep learning models and data pipelines for industrial customers by working directly with them on-site to solve complex engineering challenges.
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.
Prep for this interview
4 questions likely to come up at PhysicsX, each matched to a line in this posting.
- 1Travel
They wrote: “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.”
How do you handle being on the road with customers for weeks at a time?
How to answer
Why they ask: Many FDE roles involve regular travel. They check you know what you're signing up for.
- Your past travel or on-site experience
- How you stay productive away from the team
- What travel level works for you, said plainly
Tip: Be honest about what works for you. A mismatch here hurts both sides later.
- 2Integrations and APIs
They wrote: “Working closely with simulation engineers to ensure seamless integration of data science models with simulations.”
Walk me through connecting our product to a customer's internal system that has no documentation.
How to answer
Why they ask: This happens weekly in the job. They want a calm, step-by-step method.
- Find who owns the system and get read access to a test environment
- Watch real traffic or logs to learn the data shapes
- Build a thin adapter with logging around every call
- Agree on how failures get reported, and who fixes what
Tip: Talk about finding the owner of the system first. The code is the easy part.
- 3Data and SQL
They wrote: “Designing, building and testing data pipelines that are reliable, scalable and easily deployable in production environments.”
How would you design a daily pipeline that feeds customer data into our product?
How to answer
Why they ask: Tests basic data engineering: scheduling, failures, re-runs and monitoring.
- Extract with timestamps or change tracking, not full dumps, when possible
- Transform in steps you can re-run without creating duplicates
- Validate row counts and key fields before loading
- Alert a person on failure, with enough detail to fix it
Tip: Make re-running safe (same input, same result) and say how you'd know it failed.
- 4Cloud, deployment and security
They wrote: “💰 5% contribution to 401(k) - build long-term security with a strong retirement plan.”
The customer's security team sends a 200-question review. How do you handle it?
How to answer
Why they ask: Security reviews often block deals. FDEs help get through them.
- Start from your company's standard answers (SOC 2 report, past reviews)
- Answer clearly; don't oversell
- Bring in your security team for anything you're unsure of
- Keep the new answers for the next customer
Tip: Reuse past answers, flag honest gaps, and offer a call for the hard questions.
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