Data Scientist

Hadrian - Manufacturing the Future Hadrian is building autonomous factories that help aerospace and defense companies manufacture rockets, satellites, jets, and ships up to 10x faster and up to 2x cheaper. By combining advanced software, robotics, and full-stack manufacturing, we are reinventing how America produces its most critical parts. We’re accelerating our mission with the launch of Factory 3 in Mesa, Arizona, a 290,000-square-foot facility creating 350 new jobs. We are expanding rapidly to support thousands of future hires, launching Hadrian Maritime to expand into naval production, and introducing a Factory-as-a-Service model that delivers complete systems instead of individual parts. Hadrian is backed by leading investors including T. Rowe Price, Lux Capital, Founders Fund, and Andreessen Horowitz, our fast-growing team is united around reindustrializing American manufacturing for the 21st century and beyond.

This is the modeling half of manufacturing data science at Hadrian. The factory turns geometry into parts: a CAD model, a material, a set of tolerances, a route through stations. This role predicts what that process will do before it runs, and gets better at it with every part that goes through. Our factories generate rich process data on high-mix, low-volume aerospace parts, but most parts are near-unique, so the classic "lots of history per SKU" playbook doesn't apply. The leverage is representation: embed a part by its geometry, material, tolerances, and route, then predict cycle time, cost, tool wear, quality, and triage risk from the parts like it, before the first chip is cut. The work spans forecasting and prediction (cycle time, tool life, quality and yield, demand, queue and lead time, always with calibrated uncertainty), representation learning (part and operation embeddings so a part with no history inherits the behavior of its neighbors), and geometric modeling (features and models straight off CAD, mesh, and point cloud). Deep models where they earn their keep, classical where it wins. Those predictions feed quoting, scheduling, capacity, and DFM, and you'll own the pipelines that serve them, partnering with ML Platform to deploy and Data Engineering on features. What You’ll Do Build and ship production models for cycle time, tool life, quality, and demand, using calibrated uncertainty (quantile, conformal, or Bayesian) rather than point estimates alone. Model directly off geometry by engineering features and building geometric/graph models that predict cycle time, cost, DFM and tolerance risk, and triage probability. Build a part and operation embedding layer that represents a part by geometry, material, tolerances, and route, retrieves similar parts, and transfers their behavior to cold-start new ones. Validate honestly through backtesting that respects time ordering and part-family leakage, and make a defensible case for deep versus classical methods on each problem. Own models end to end on the platform, including reproducible training, serving, monitoring, and retraining, in partnership with ML Platform and Data Engineering. Close the loop in production by detecting drift and quality anomalies so predictions improve as new data lands. Turn predictions into decisions for quoting, scheduling, capacity, and DFM; design experiments and A/B tests to measure real impact, then document and hand off to operations. What We’re Looking For Forecasting and prediction on real, messy manufacturing data, with honest uncertainty. Representation learning and embeddings; similarity and retrieval; transfer/few-shot for sparse data. Deep learning that ships (PyTorch), and the judgment to know when not to use it. Strong classical ML and statistics (GBMs, Bayesian/hierarchical, survival, causal). Validation done right: backtesting, leakage control (time and part-family), calibration. Python; turns a messy process into features and a model into a decision an operator or a downstream system can