Senior Data Scientist II

We are seeking a Senior Data Scientist II to lead the design and validation of AI-driven product capabilities within the legal domain. This role focuses on defining what to build and why-leveraging machine learning, NLP, and large language models (LLMs) to solve complex legal workflows. You will drive experimentation, modeling, and evaluation, partnering closely with engineers to translate validated approaches into scalable, customer-facing solutions.

Lead experimentation and model development for AI/ML solutions in legal products.

Design and evaluate NLP, LLM, and generative AI approaches (e.g., RAG, prompt strategies).

Define agentic workflows and reasoning strategies for multi-step legal tasks.

Define retrieval strategies, including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality).

Analyze large-scale legal datasets to extract insights and improve model performance.

Establish best practices for model evaluation, validation, and benchmarking.

Translate experimental results into clear product recommendations and business impact.

Collaborate with product, legal experts, and engineers to align solutions with user needs.

Mentor team members and provide technical leadership in data science and AI.

Strong experience in machine learning, NLP, and LLM-based modeling.

Proven experience designing and running experiments, including model evaluation and iteration.

Experience with generative AI techniques (e.g., prompt engineering, RAG).

Experience designing and evaluating hybrid search (semantic + lexical) using embeddings and vector databases.

Experience designing agentic workflows and reasoning strategies, with hands-on experience applying agent frameworks (e.g., LangChain, LangGraph, AutoGen) in real-world use cases.

Proficiency in Python and data analysis tools.

Strong problem framing and experimental design skills.

Ability to translate ambiguous problems into structured AI solutions.

Clear communication of technical findings to diverse stakeholders.

Collaboration with engineering to bridge research and production.

Ownership of model quality, accuracy, and business impact.

Strong foundation in statistics, modeling, and large-scale text processing.