Lead Data Scientist

We’re seeking a Data Science Leader to guide a small, high-performing team building LLM-powered applications for legal workflows. This role blends hands-on technical work with project leadership, cross-functional collaboration, and mentoring. You’ll run multiple projects within the team, partner closely with SMEs, engineering, and product, and ensure we’re shipping high-quality GenAI solutions.

Leadership & Team Execution

• Lead and mentor data scientists and ML engineers through coaching, code reviews, and technical guidance.
• Manage multiple GenAI projects from ideation to production deployment.
• Partner with product, engineering, and legal teams to define requirements, scope, and success criteria.
• Establish and enforce strong engineering, MLOps, and evaluation best practices including documentation, testing, and reproducible experimentation.

Hands-On Technical Work

• Build and deploy LLM-powered applications supporting complex legal workflows.
• Fine-tune, benchmark, and deploy LLMs; design RAG architectures, prompt frameworks, and evaluation pipelines.
• Architect and maintain data pipelines for preprocessing, labeling, and managing large legal document datasets.
• Run experiments, analyze model performance, and drive iterative improvements.
• Integrate models into production environments using APIs, orchestration frameworks, and cloud infrastructure.
• Maintain high-quality datasets for training, validation, and continuous model improvement.

Cross-Functional Collaboration

• Work closely with legal experts to translate domain requirements into practical ML solutions.
• Collaborate with engineering teams on architecture, infrastructure, and deployment planning.
• Clearly communicate results, risks, and progress to technical and non-technical stakeholders.

Requirements

• 5–8+ years of experience in Data Science, Machine Learning, or Applied AI, with hands-on LLM/GenAI experience.
• Experience leading or mentoring a technical team.
• Strong Python skills and experience with PyTorch or TensorFlow, plus Hugging Face tooling.
• Practical experience with prompt engineering, RAG pipelines, LLM fine‑tuning, and evaluation.
• NLP experience with models and libraries such as BERT, spaCy, word2vec, embeddings, and text classification.
• Hands-on experience working with leading LLM ecosystems, including OpenAI (GPT models), Anthropic (Claude), and Google DeepMind’s Gemini, plus associated APIs or cloud platforms (Azure OpenAI, Vertex AI, AWS Bedrock, etc.).
• Familiarity with relational databases, NoSQL systems, and vector stores.
• Cloud experience in AWS, GCP, or Azure.
• Experience with distributed computing frameworks (Spark, Ray) is a plus.
• Experience with ML Ops / LLM Ops tools like MLflow, DVC, LangFuse, or similar.
• Excellent communication skills and ability to work with non-technical teams.

Preferred Qualifications

• Master’s degree in Computer Science, Data Science, ML, Statistics, or related discipline.
• Experience working with legal tech, compliance-heavy industries, or sensitive/high‑risk data environments.