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Senior Data Scientist II

Datum Software, Inc.United States🇺🇸United StatesPosted Oct 12, 2026

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

Seniority
Mid Senior
Work mode
Hybrid
Location
United States
Posted
21 hours ago
SQLAWSMachine LearningNLPScikit-learnGitLLMPyTorchPython

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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