Data Scientist - Experimental Physics

About Xanadu:
Xanadu’s mission is to build quantum computers that are useful and available to people everywhere.

At Xanadu, we are learners, innovators, researchers, collaborators and problem solvers. We are creating something that has never been built before. What we are doing is extremely hard, the classic moon shot. Few people in their life will be able to be a part of something like this, where if we are successful, the technologies we develop will solve some of the world’s most challenging problems, and literally change the world.

And that is something to be excited about!



Your role and responsibilities:
The successful candidate will join the cross-functional Data Analysis team to develop statistical models and measurement strategies that guide photonic component design. Working with photonics designers and experimentalists, you will distinguish measurement uncertainty from fabrication variability and translate experimental data into actionable design decisions.
The ideal candidate is an impact-driven data scientist capable of abstracting complex hardware problems, isolating leading-order effects from secondary issues, and translating those priorities into statistical models. Working closely with photonics designers and experimentalists, you will define the next generation of ultra-low-loss component designs and measurement procedures, directly impacting design optimizations at Xanadu's 200mm and 300mm fabrication partners.

You will:

• Develop statistical models to infer the characteristics of photonic devices from experimental datasets.
• Partner with photonics designers to develop measurement methods and test structures that distinguish competing explanations, reveal systematic errors, and improve parameter-estimation precision.
• Investigate reported uncertainties by finding and quantifying unknown systematics, building models to account for them, and devising methods to reduce errors.
• Build and maintain robust Python-based code to analyze measured data.

Basic qualifications and experience:

• MSc or PhD in physics, statistics, applied mathematics, engineering, or a related quantitative discipline.
• At least two years of experience analyzing experimental or observational data to answer scientific or engineering questions (relevant graduate research counts toward this experience).
• Demonstrated ability to formulate statistical models connecting physical processes and measurement systems to observed data, identify dominant effects, and evaluate model assumptions.
• Strong applied knowledge of parameter estimation and uncertainty quantification, including identifying and modeling systematic effects, accounting for correlated errors, and propagating uncertainties into derived quantities.
• Experience solving inverse problems and assessing the reliability of inferred parameters through model diagnostics, sensitivity analysis, and validation against simulations or independent measurements.
• Experience designing or improving experiments, measurement procedures, or validation studies to distinguish competing explanations and reduce uncertainty.
• Proficiency in Python for scientific computing and data analysis, with experience developing reproducible, maintainable analysis code using version control, documentation, and appropriate tests.
• Ability to work with scientists and engineers across disciplines, translate open-ended hardware questions into tractable analyses, and communicate conclusions, limitations, and recommended next steps. Ability to prioritize work according to its expected impact on research and engineering decisions.

Preferred qualifications and experience:

• Experience with hierarchical or multilevel models to distinguish sources of variation, such as measurement repeatability, device-to-device…