Principal Data Scientist

Physicsx · Londonsenior

In short

▸Científico de datos principal que lidera investigación en simulación física con IA.
▸Trabajas con ingenieros y clientes para convertir problemas de ingeniería en modelos predictivos avanzados.
▸Destaca por liderar equipos y traducir hallazgos a producción en industrias de alto impacto.

Proficiency in English is required for collaboration and communication.

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What they ask for

✓Doctorado en ciencia, matemáticas, física o ingeniería.
✓Experiencia >4 años en roles data-driven.
✓Experto en aprendizaje profundo o métodos probabilísticos para PDEs.
✓Habilidades sólidas en modelado de sistemas físicos con IA.
✓Capacidad para liderar equipos y guiar a colegas junior.
✓Comunicación efectiva con equipos técnicos y clientes.

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

machine learningdeep learningprobabilistic methodsPDEsoperator learningneural operatorsgeometric deep learning3D computer visionpoint-cloudmesh-structured data

Who should you write to at Physicsx?

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About us PhysicsX is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software. We are building an AI-driven simulation software stack for engineering and manufacturing across advanced industries. By enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle, PhysicsX unlocks new levels of optimization and automation in design, manufacturing, and operations — empowering engineers to push the boundaries of possibility. Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive. Note: We are currently recruiting for multiple levels and positions, however please only apply for the role that best aligns with your skillset and career goals. What you will do • Own Research work-streams at a high-level to deliver outcomes. • Align priorities with problem stakeholders, internal and external. • Set the technical direction for the stream and apply judgement and taste to drive progress. • Plan roadmaps with clear milestones for key decisions and outcomes. • Organise and guide the more junior members of the team to effectively execute and deliver against this roadmap. • Communicate purpose and key outcomes to raise awareness across the company and create opportunities for use and deployment. • Contribute towards Research group strategy and culture. • Identify research areas that would be valuable to the company and champion their development, ordering wrt other research objectives. • Promote effective working patterns and proactively flag issues with team dynamics to foster a productive environment. • Nurture younger colleagues to grow their skillset and guide their professional development. • The below activities in particular. • Work closely with our machine learning engineers, simulation engineers, and customers to translate physics and engineering challenges into mathematical problem formulations. • Build models to predict the behaviour of physical systems using state-of-the-art machine learning and deep learning techniques. • Discuss the results and implications of your work with colleagues and customers, especially how these results can address real-world problems. • Collaborate with colleagues beyond the research team to translate your models into production-ready code. • Communicate your work to others internally and externally as called for in paper publication venues, industry workshops, customer conversations, etc. This will involve writing for academic and non-academic audiences. What you bring to the table • Ability to scope and effectively deliver projects. • Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering. • 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. • PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in any of the following: • operator learning (neural operators), or other probabilistic methods for PDEs; • geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data; • generative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non-parametric, scaling to large datasets, etc.). • Ideally, >4 years of experience in a data-driven role in a profession

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