Senior Data Scientist - Operations Research

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 optimisation and forecasting models that improve the movement of wafer lots through our fabrication partners. Working closely with the fabrication team, you will help reduce cycle times, anticipate delivery milestones, and adapt lot priorities as fab conditions change.

The team makes dynamic decisions about when to start lots, which wafers to prioritise, and how to use limited fabrication and metrology capacity. You will use operational data and engineering knowledge to formulate models, test decision strategies, and produce reliable forecasts and actionable recommendations. Your work will help answer questions such as: When will a lot reach its next milestone? Which priority changes could improve delivery timelines? When should new lots enter the line? Lot-flow optimisation and forecasting is the immediate priority, but as the team grows you will take on new modelling problems and help shape how data science is applied across Xanadu.

The ideal candidate is an impact-driven data scientist who can translate a complex, changing engineering environment into practical models. Your primary focus will be model formulation, validation, and evaluation. You will bring a strong foundation in statistics and predictive modelling, with particular depth in optimisation and forecasting. You will choose methods appropriate to the problem, critically evaluate their assumptions, and develop high-quality prototypes that demonstrate their value.

You will:

• Own wafer-lot optimisation and forecasting models, defining objectives, constraints, trade-offs, and success criteria with engineering stakeholders.
• Forecast lot milestones and completion dates, quantifying uncertainty as fab conditions change.
• Optimise lot releases, priorities, and resource allocation under queue, hold, route, and capacity constraints.
• Analyse operational and metrology data; use historical data and simulation to identify bottlenecks, validate models, and compare strategies against current practice.
• Develop Python-based prototypes, documenting models and validation results for engineering handover.
• Communicate actionable recommendations and limitations, measure operational impact, and refine models with stakeholder feedback.
• Scope unfamiliar problems and adapt methods as evidence and priorities evolve.

Basic qualifications and experience:

• MSc/PhD, or BSc with equivalent industry experience, in a quantitative discipline.
• 3+ years applying data science to scientific, engineering, or operational problems, with substantial experience in optimisation and forecasting.
• Experience independently leading ambiguous modelling problems from formulation through validation to decisions informed by the results.
• Strong statistical inference and predictive modelling skills, including uncertainty quantification, diagnostics, and validation.
• Experience working with unstructured and incomplete data.
• Ability to formulate constrained optimisation problems, select methods, and assess feasibility, sensitivity, and practical value.
• Experience validating forecasts under changing conditions and incomplete observations while avoiding data leakage.
• Proficiency in Python and reproducible model development, including version control, documentation, and testing; experience preparing datasets from multiple sources, including event histories.
• Ability to collaborate across disciplines, communicate recommendations and limitations, and prioritise work by impact.

Preferred qualifications and experience:

• Experience transferring modelling methods across domains and learning unfamiliar subject matter.
• Experience with mathematical or constraint programming, scheduling heuristics, stochastic or robust optimisation, or multi-objective optimisation.
• Experience with manufacturing scheduling, capacity planning, supply-chain optimisation, discrete-event simulation, or queueing models.
• Experience with time-to-event forecasting, including survival analysis.
• Familiarity with semiconductor fabrication, photonics, process integration, or metrology, including using process data to guide wafer disposition.
• Experience evaluating models with engineers and defining requirements for implementation.

This is for a new position. Your base salary will be determined based on your location, experience, and internal benchmarks. The base salary range is 150,000 - 200,000 CAD. You will also be eligible for equity and benefits.
Our values are important. They are fundamental and lay the foundation for culture at Xanadu. Learn more about our values here.

We are an equal opportunity employer and encourage candidates of all backgrounds to apply. We are committed to building an inclusive, safe, and equitable culture and fostering an environment where our employees feel included, valued, and heard. We are committed to meeting the needs of all individuals and support a barrier-free workplace. Should you require accommodations at any point during the recruitment process please contact Recruiting at recruiting@xanadu.ai.

Please be advised that we may use artificial intelligence (AI) tools to assist in the screening and assessment of applicants for this position. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.