Data Scientist

Hadrian Automation • Torrance, CA

6d • $170,000 - $300,000

About The Position

This role focuses on the modeling aspect of manufacturing data science at Hadrian. The factory transforms geometry into parts, and this role predicts the outcome of that process before it runs, continuously improving with each part produced. Given that most parts are unique, the challenge lies in leveraging representation learning to predict cycle time, cost, tool wear, quality, and risk by embedding parts based on their geometry, material, tolerances, and route, and then predicting based on similar parts. The work involves forecasting and prediction with calibrated uncertainty, representation learning for part and operation embeddings to handle cold-start scenarios, and geometric modeling using features directly from CAD, mesh, and point cloud data. Deep learning models will be used where beneficial, alongside classical methods. The predictions generated will inform quoting, scheduling, capacity planning, and design for manufacturability (DFM), and the role includes owning the pipelines that serve these predictions, collaborating with the ML Platform team for deployment and Data Engineering for feature development.

Requirements

• 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 consume.
• Deploys and monitors models; thinks about pipelines and drift from the start, not after.
• Works with limited, high-value data and knows how to borrow strength.

Nice To Haves

• Geometric deep learning: mesh / point-cloud networks, GNNs, PyTorch Geometric
• CAD / B-rep, feature recognition, and turning part geometry into ML features
• Retrieval and ANN at scale; embedding stores
• Bayesian and hierarchical modeling for small data; physics-informed ML
• Survival and reliability modeling (tool life, degradation)
• Aerospace or precision-manufacturing background; DFM intuition
• Digital twins and simulation; causal inference; sensor / IoT data

Responsibilities

• 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.

Benefits

• Medical, dental, vision, and life insurance plans for employees
• 401k
• Relocation support may be provided for certain situations, based on business need.
• Flexible vacation policy
• Equity

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed