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Auger

Applied Scientist, Artificial Intelligence Economics

Bellevue, WA
Python scikit-learn XGBoost LightGBM PyTorch Prophet NLP Machine Learning SQL Airflow dbt API Streaming
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Description

Applied Scientists - AI Economics

Location: Bellevue

Department: Applied Science & Economics

Location Type: IN_OFFICE

Employment Type: FULL_TIME

About the Role


The AI Economics Team at Auger is building an AI and agentic-powered market intelligence system that enables our customers to forecast supply chain risk, optimize sourcing decisions, and respond to global disruptions in real time. We are looking for an Applied Scientist to design, deploy, and scale the systems at the core of this platform.

You will own the end-to-end ML lifecycle for production systems including demand and cost forecasting models, supplier reliability prediction, NLP pipelines for news and document intelligence, and real-time cost calculation engines. Your work will directly inform sourcing recommendations that move billions of dollars in procurement spend.

This role sits at the intersection of applied ML and economics research and production engineering. You will collaborate closely with economists on causal inference methodology, with data engineers on feature pipelines, and with product teams to translate model outputs into actionable customer-facing tools. You should be comfortable shipping models that run at scale, monitoring their performance in production, and iterating based on real-world feedback.

Our current technical stack includes time-series forecasting (demand signals, commodity prices, delivery timing), causal ML for supplier effect estimation, NLP for document parsing and news sentiment (certificates of origin, compliance documents, global news feeds), and real-time scoring APIs serving predictions to interactive dashboards.

What You Bring


  • 4+ years experience building and deploying ML models in production environments
  • Strong proficiency in Python and ML frameworks (scikit-learn, XGBoost/LightGBM, PyTorch)
  • Experience with time-series forecasting methods (ARIMA, Prophet, gradient boosting, neural forecasters)
  • Familiarity with NLP pipelines: text classification, named entity recognition, document parsing, or sentiment analysis
  • Production ML infrastructure: model serving, feature stores, monitoring, retraining workflows, and deployment into existing CI/CD systems(including unit tests and integration tests)
  • SQL proficiency and experience with data pipeline tools (Airflow, dbt, or similar)
  • Ability to communicate model behavior and limitations to non-technical stakeholders

Auger
Auger

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