PhysicsX
PhysicsX11 open roles

Principal Data Scientist / Algorithm Engineer (Delivery)

SingaporeOn-siteSeniorFull-time

Posted yesterday

The role in short

Lead the development and deployment of production-grade deep learning models and data pipelines for industrial customers, working directly on-site to integrate AI solutions into their engineering workflows.

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 Principal Data Scientist (Algorithm Engineer) in Delivery, you are an experienced problem solver and technical leader who is passionate about building practical solutions that enable customers to make better engineering decisions. You are someone who can grasp advanced engineering concepts across multiple industries, lead technical initiatives, and excel at working directly with customers (and often side-by-side with them on-site) to embeds cutting edge AI models into tools that are useful and used.

You've consistently tackled difficult problems that require strong foundations in data driven modelling and deep learning techniques, with extensive hands-on experience in probabilistic methods and predictive modelling. Your expertise in python, along with advanced proficiency in libraries like NumPy, SciPy, Pandas, TensorFlow and PyTorch, is essential, with proven ability to architect and deploy scalable, production-ready models and data pipelines.

With industry experience (post Masters of PhD) in a commercial, you're ready to not only execute but also lead and mentor others. You're truly excited about growing both your technical expertise and leadership skills, naturally taking ownership of complex data science work streams and guiding teams to success. You continuously improve the systems and solutions you work on to ensure they are practical, impactful and meet the evolving needs of our customers.

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, while providing technical leadership to your team.

You'll build the foundations for successful, impactful solutions by:

  • Leading pre-processing and analysis of complex data to prepare it for use in predictive modelling, establishing best practices and methodologies for your team.
  • Architecting and developing innovative deep learning models in combination with state-of-the-art optimisation methods to predict and control the behaviour of physical systems.
  • Taking full responsibility for the quality, accuracy and impact of your work and the work of your team.
  • Designing, building, and testing data pipelines that are reliable, scalable, and robustlyeasily deployable in production environments.
  • Leading cross-functional collaboration with simulation engineers to ensure seamless integration of data science models with simulations.
  • Driving internal R&D and product development, helping to refine models and identify new areas of application.
  • Mentoring junior team members and providing technical guidance to help them grow.
  • Leading open communication and presentations with both technical teams and customers, helping onboard users and co-develop with customers.
  • Representing PhysicsX as a technical authority when traveling to customer sites in North America, Europe, Asia, Oceania, an average of 2-3 weeks per quarter, where you'll collaborate closely with customers to build solutions on site.

As a senior member of the team, you'll have significant influence on our technical direction and the opportunity to shape future solutions and products, while developing your skills as a technical leader.

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!

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

2 questions likely to come up at PhysicsX, each matched to a line in this posting.

  1. 1Integrations and APIs
    They wrote: “Leading cross-functional collaboration 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.

    1. Find who owns the system and get read access to a test environment
    2. Watch real traffic or logs to learn the data shapes
    3. Build a thin adapter with logging around every call
    4. 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.

  2. 2Data and SQL
    They wrote: “Designing, building, and testing data pipelines that are reliable, scalable, and robustlyeasily 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.

    1. Extract with timestamps or change tracking, not full dumps, when possible
    2. Transform in steps you can re-run without creating duplicates
    3. Validate row counts and key fields before loading
    4. 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.

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