Data Scientist

About the Role

We are seeking a Data Scientist to join the Applications Engineering team. In this role, you will build and deploy machine learning models that turn complex, high-dimensional sensor signals into actionable predictions about material properties, bridging the gap between raw instrument data and the intelligence manufacturers act on. This is a forward-deployed, customer-adjacent role. You will work directly with customer samples and datasets to execute proof-of-concept studies, validate model performance on novel materials, and translate results into product improvements. You will collaborate closely with software and hardware engineering teams to move models from research into production.

Responsibilities

• Model Development & Calibration
• Build, calibrate, and validate predictive models that map sensor signal features to material properties
• Design and evaluate new model architectures and featurization strategies suited to small-data, high-dimensional scientific datasets
• Apply methods including regression, dimensionality reduction, probabilistic modeling, anomaly detection, and physics-informed ML
• Model Validation & Production Readiness
• Develop testing and validation frameworks for model performance, including uncertainty quantification and out-of-distribution detection
• Characterize model robustness across sample types, process conditions, and instrument configurations
• Prepare models and documentation for handoff to the software engineering team for production deployment
• Customer-Facing Proof-of-Concept Work
• Analyze datasets from customer proof of concepts
• Compile technical reports and supporting materials to deliver to customers
• Translate findings and stakeholder feedback into model improvement roadmaps

Qualifications

• B.S. in Data Science, Statistics, Applied Mathematics, or a related quantitative field with 3 to 5 years of applied ML/data science experience; or M.S. with 1 to 3 years (Ph.D. a plus, not required)
• Hands-on experience building and validating predictive models (supervised and self-supervised) in Python
• Solid grasp of statistical modeling: uncertainty quantification, regularization, covariate analysis, and feature importance methods
• Strong communicator, comfortable presenting technical findings to both technical and non-technical audiences

Prior Experience

• Experience working with time-series, spectroscopic, or other sensor-based signal data
• Prior work in manufacturing, materials science, energy storage, semiconductors, or another physical science domain
• Prior customer-facing or applications engineering experience in a technical product company
• Experience deploying models in production software environments
• Familiarity with data pipeline development (PostgreSQL or similar)
• Fluency in Mandarin Chinese, Japanese, German, Korean, or another key stakeholder language