- Own risk and prediction models that flag customs hold and temperature excursions.
- Build dynamic re-routing optimization for flagged exceptions.
- Own data and feature infrastructure for model-ready signals.
- Develop experimentation systems for model evaluation and metric definition.
Requirements
- 3-9 years of applied data science or ML experience
- Experience shipping models to production
- Strong in Python and modern data and ML stack
- Comfortable owning data infrastructure: pipelines, feature engineering
- Real experimentation rigor and ability to design meaningful tests
- Understanding of data modeling and system connections
- Must be based in San Francisco or Chicago and excited about being in the office.
Core Competencies
Demonstrates expertise in data science and machine learning, with a strong focus on building and optimizing risk and prediction models. Proficient in Python and data infrastructure management, including pipelines and feature engineering.
Highest-signal resume keywords
- Data Science Experience
- Machine Learning Model Deployment
- Python Proficiency
- Data Infrastructure Ownership
- Experimentation Design
ATS Optimization Keywords
Hard Skills
- Data Science
- Machine Learning
- Model Deployment
- Feature Engineering
- Data Modeling
Soft Skills
- Problem Solving
- Analytical Thinking
Industry Keywords
- Risk Models
- Prediction Models
- Temperature Excursions
- Dynamic Re-Routing Optimization
Tools & Technologies
- Data Pipelines
- Experimentation Systems