Data & Signal Analysis Scientist - Q

Summary

Our team is new, growing, and evolving quickly, and we are hiring across multiple disciplines. We work at the intersection of machine learning, hardware, and software, creating technologies that reshape how people communicate and experience the world around them in everyday life.

Minimum Qualifications

MSc or PhD in physics, electrical engineering, or a related quantitative field. 3+ years of relevant industry experience. Strong signal processing background (time-frequency analysis, noise, coherence, filtering). Experience with large-scale data analysis Strong understanding of experimental physics and measurement systems, enough to interpret results and spot artifacts. Solid statistics: hypothesis testing, regression, uncertainty estimation, and feature selection. Ability to work independently on open-ended problems.

Preferred Qualifications

Machine learning frameworks (scikit-learn, PyTorch) and model interpretability techniques. Experience with optical, photonic, or sensor data. Distributed or high-volume data processing. Occasional hands-on lab experience.

Description

We are looking for a scientist who can turn experimental results into deep physical insight. Working closely with experimental teams gathering raw data, you will analyze multi-source datasets to uncover the connections between signals, derived features, and system-level performance metrics. You will combine data-driven modeling with first-principles physical intuition to explain performance dynamics and isolate the root causes driving them.

Responsibilities

Analyze large, multi-source datasets of signals and features. Build scalable, reproducible pipelines. Find and quantify relationships between signal characteristics, extracted features, and performance outcomes. Apply non-linear and data-driven methods to uncover effects that linear analysis misses. Build and validate models that explain or predict performance from signal-level and feature-level inputs. Assess data quality and measurement uncertainty. Recommend which measurements or experiments to run next. Separate causal effects from correlations through ablations and structured analysis. Present findings and recommendations clearly to physics, algorithm, and systems teams. Flag inconsistencies in the physics or the data early.