Data Scientist (ML/AI)

Overview

Lead the development of machine learning solutions across OEC's Mechanical business, spanning personalization , search and recommendations , forecasting , NLP , fraud detection , operational efficiency , and GenAI . This is an individual contributor role on OEC's Applied AI team , with end-to-end ownership from problem framing through production deployment and measured business impact.

What You'll Do

• Build and optimize search , ranking , recommendation , and personalization models .
• Develop forecasting models for demand , growth , conversion , and churn .
• Apply NLP and entity resolution to catalog, fitment, and repair data.
• Design fraud detection , anomaly detection , and operational AI solutions .
• Run experiments , A/B tests , and causal analyses to measure impact.
• Prototype and evaluate LLM and agentic AI solutions .
• Own models end-to-end , from development through deployment and monitoring .
• Mentor other data scientists and establish modeling best practices .
• Partner with Product , Engineering , and business leaders to deliver measurable outcomes .

What You Bring

• 3+ years of applied data science or machine learning experience.
• Proven success deploying production models that improved business metrics .
• Expertise in several of the following:
• Search, ranking, recommendations, and personalization
• Forecasting and predictive modeling
• NLP and text classification
• Fraud, risk, or anomaly detection
• Experimentation and causal inference
• LLMs and AI evaluation frameworks
• Strong Python , SQL , AWS , Airflow , and MLflow experience.
• Experience owning ML systems from development through production.
• Demonstrated technical leadership , mentorship , and stakeholder management .

Nice to Have

• Master's degree or PhD in a quantitative field.
• Experience in B2B commerce , marketplaces , or automotive .
• Elasticsearch/OpenSearch , SageMaker , Azure , Fabric , or Terraform experience.
• Experience with agentic AI systems , real-time inference , and complex product catalogs .

How We Work

• We value collaboration , ownership , and pragmatic problem-solving.
• We prioritize shared success over individual wins.
• We expect scientists to ship and own their work in production .
• We are measurement-first and evidence-driven.
• We encourage experimentation , continuous learning , and challenging assumptions.
• We leverage AI-assisted development while maintaining high standards for quality , reliability , and accountability .