Senior Data Scientist

Responsibilities

• Design, build, and productionize ML models for problems like ETA/ATA prediction, using regression, classification, and time-series forecasting techniques
• Develop NLP/LLM-based extraction pipelines for message-based ETA and status updates (text extraction, entity recognition)
• Own models end-to-end: data pipeline → training → deployment → monitoring → retraining
• Work with noisy, real-world logistics and supply chain data (GPS pings, check calls, carrier data) rather than clean, pre-processed datasets
• Diagnose gaps between offline evaluation performance and live production accuracy, and drive fixes
• Build and maintain automated training/retraining pipelines using orchestration tools such as Airflow
• Set up and maintain model monitoring and observability (e.g., Grafana) to catch drift and degradation proactively
• Replace manual or rule-based processes with ML-driven automation (e.g., automating manual check calls)
• Translate model performance improvements into business impact — operational savings, efficiency gains, and deal-relevant outcomes
• Mentor and guide other data scientists/engineers on technical approach and best practices

Languages