Why this role stands out
This hybrid role offers exciting opportunities to develop cutting-edge AI solutions in legal tech, leveraging your expertise in large language models and NLP. You'll thrive here if you're a collaborative data scientist eager to make a tangible impact on innovative workflows within a reputable company. Apply today to advance your career in a dynamic and forward-thinking environment.
Quick overview
Job description
Job Details:
Job Title: Senior Data Scientist II
Duration: Long-Term Contract
Location: Raleigh, NC || Onsite / Remote (EST Only)
Note: Preference will be given to candidates with Mandarin language proficiency.
Job Description:
Position Summary
- Legal & Professional is seeking a hands-on Data Scientist to develop and evaluate AI solutions for legal content workflows.
- The role focuses on large language models, natural language processing, and reliable data pipelines.
- Experience with traditional machine learning is a strong plus.
- The successful candidate will collaborate with engineers, product stakeholders, and legal subject matter experts to translate business requirements and editorial feedback into measurable improvements.
- This role requires strong Python skills, sound experimental judgment, and the ability to independently investigate issues and deliver working solutions.
Key Responsibilities
- Develop and improve LLM-based workflows for information extraction, classification, legal summarization, and content generation.
- Design and evaluate prompts, retrieval-augmented generation (RAG), and multi-step agent workflows.
- Build reliable data-preparation pipelines, including parsing, transformation, validation, caching, and recovery from interrupted processing.
- Create representative evaluation datasets and conduct controlled experiments, error analysis, and regression testing.
- Work with subject matter experts to assess factual accuracy, completeness, source support, and consistency, and translate feedback into generalizable improvements.
- Diagnose problems across data, retrieval, model behavior, and application logic.
- Balance output quality, latency, and cost when comparing technical approaches.
- Maintain tested, version-controlled code and clear documentation to support reproducibility and team handoffs.
Required Qualifications
- Bachelor’s degree or higher in computer science, Data Science, Statistics, Mathematics, or a related quantitative field, or equivalent practical experience.
- At least 3 years of relevant experience in data science, machine learning, or NLP, including hands-on experience developing LLM-based applications.
- Strong Python programming skills and experience with SQL and structured or semi-structured data, such as JSON and XML.
- Practical experience with prompt design, structured outputs, retrieval, and systematic LLM evaluation.
- Understanding of statistical evaluation, train/validation/test separation, data leakage, and appropriate performance metrics.
- Experience using Git, debugging existing code, and writing automated tests.
- Ability to communicate findings clearly, incorporate feedback, and proactively raise delivery risks.
Preferred Qualifications
- Experience with traditional machine learning, including feature engineering, classification, tree-based models, and imbalanced datasets.
- Familiarity with scikit-learn, PyTorch, or comparable frameworks.
- Experience fine-tuning transformer models for classification, information extraction, or sequence labeling.
- Familiarity with AWS, particularly S3 and EC2, and Linux or GPU-based training environments.
- Experience with legal documents, editorial workflows, or other accuracy-sensitive domains.
What are the top 3 must-have skills?
- Applied LLM development: demonstrated experience building and evaluating workflows involving prompts, retrieval, and structured generation.
- Python and data engineering: ability to independently build, debug, and maintain reliable data-processing and model-integration code.
- Experimental design and evaluation: ability to compare baselines, analyze errors, validate improvements, and communicate actionable findings.
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