Data Scientist II

About the role

The Data Scientist II will work closely with Data Science, Product, and Engineering to build and improve ML and AI systems that drive operational value. This role is ideal for a hands-on practitioner with applied experience in NLP and LLM-based systems ready to take on meaningful technical ownership.

Responsibilities

• Develop, evaluate, and iterate on NLP and LLM-based systems including text classification, information extraction, and context retrieval pipelines
• Build measurement and evaluation frameworks (offline and online) to assess system performance and quantify improvement impact
• Develop golden test datasets and define methodologies for creating/maintaining them over time
• Evaluate and apply appropriate approaches for language tasks (prompt engineering, fine-tuning, or classical NLP methods including RAG architectures)
• Perform structured analysis of system performance to surface failure modes, data gaps, and high-value investment areas
• Partner with engineers to support deployment, integration, and monitoring of ML/AI systems in production
• Contribute to standards and best practices for deploying, evaluating, and monitoring text-based ML systems
• Document work clearly and maintain knowledge artifacts
• Collaborate with senior data scientists and cross-functional partners to translate business needs into technical solutions

Qualifications

• Bachelor's or Master's degree in a quantitative field (computer science, statistics, linguistics, or related) and 2–4 years of applied ML/data science experience, or equivalent practical experience
• Hands-on experience building or improving NLP or LLM-based systems in applied settings
• Familiarity with text classification, information extraction, or other NLP tasks
• Experience with both prompt engineering and fine-tuning approaches for language tasks
• Familiarity with modern retrieval strategies, RAG architectures, and how they affect LLM performance
• Experience with Hugging Face Transformers for text classification or related NLP tasks
• Experience contributing to evaluation frameworks, test sets, or performance diagnostics for ML systems
• Proficiency in Python and SQL
• Strong written communication skills
• Experience designing data annotation workflows, labeling guidelines, or label quality processes
• Experience with model deployment, monitoring, or production ML workflows
• Familiarity with LangChain and LangSmith or similar LLM orchestration tooling
• Transportation or logistics industry experience