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Job Details:
Job Title: Manager (Data Science with AI)
Duration: Full Time / Permanent Role
Location: Raleigh, NC || Hybrid
Job Description:
Typically requires:
Job Details:
Job Title: Manager (Data Science with AI)
Duration: Full Time / Permanent Role
Location: Raleigh, NC || Hybrid
Job Description:
Typically requires:
- 8 years of relevant experience in data science, machine learning, or applied AI
- 4 years of leadership experience (direct or indirect team management)
- We recognize that exceptional candidates may follow non-traditional paths and value demonstrated impact, technical depth, and leadership over strict credential requirements. Success in this role requires:
- Leading through both technical expertise and organizational influence
- Acting as a change agent, embedding best practices into workflows and systems
- Driving both team development and strategic outcomes across a broad scope
- Ability to select the right tools and technologies to solve business problems
- Proficient with Python, ML and LLM tooling such as Google ADK, LangChain, ML Frameworks (e.g. TensorFlow, PyTorch) and prompt tuning techniques.
- Familiarity with vector databases, knowledge graphs, and hybrid retrieval architecture.
- Strong experience working with structured and unstructured data at scale.
- Ability to design and implement data pipelines and preparation workflows.
- Experience integrating ML into complex, multi-stage processing systems
- Working knowledge of containerization, CI/CD, RESTful API Design and model serving tools.
- Cloud infrastructure experience on AWS (preferred), Azure, or Google Cloud Platform.
- Familiarity with AI Coding Tools (e.g. GitHub CoPilot, Claude Code, OpenAI Codex)
- Graduate degree in Computer Science, AI, Machine Learning, or equivalent experience.
- 8 years of post-degree experience, with 4 years in a data science or applied AI leadership role, with a focus on NLP/LLM systems.
- Prior experience in legal tech, legal AI, or document-intensive domains is highly desirable.
- Familiarity with ethical/legal considerations in deploying generative AI in professional settings.
- Set the vision and strategic priorities, acting as a recognized expert for Data Science
- Lead and develop a team of data scientists and ML engineers, setting the cultural tone for the group
- Drive applied research with a clear path to production, explicitly balancing innovation against real-world constraints including latency, cost, and reliability
- Build and scale evaluation science capabilities within the team, including offline evaluation frameworks, automated benchmarking pipelines, and human-in-the-loop feedback systems to rigorously measure model quality and business impact
- Champion hands-on rapid prototyping and iteration
- Collaborate with other Data Science teams to maximize re-use of components and patterns, eliminating waste, duplication and unnecessary customization
- Operate with broad scope, coordinating across multiple cross-functional teams, systems, and domains
- Collaborate closely with other Data Science teams, to define and execute the AI roadmap across the content lifecycle, maximizing reuse in areas including:
- Content collection (e.g. "web scraping”) and transformation
- Metadata extraction, enrichment, and classification
- Agentic workflows turning real-world events and legal content into legal intelligence
- AI-powered downstream product capabilities
- Design and deploy scalable, production-grade AI systems, including:
- LLM-powered document understanding and generation
- Agentic workflows balancing agent autonomy and efficiency with required structure and accuracy
- Retrieval-augmented generation (RAG) pipelines
- Hybrid ML rules-based systems for structured content
- Actively writing code, not just delegating
- Building and demoing working prototypes (e.g. by "vibe coding”)
- Directly contributing to experiments and production models
- Establish and scale best practices in Data Science, including:
- Model development, evaluation, and monitoring
- Prompt engineering and experimentation frameworks
- Data preparation and feature engineering standards
- Reusable components and platform capabilities
- Partner closely with engineering, architecture, and product leaders to:
- Integrate AI into large-scale distributed systems
- Ensure performance, scalability, and reliability
- Align technical solutions with business outcomes
- Translate complex, ambiguous problems into clear project plans and executable solutions, and lead teams through delivery
- Present tradeoffs, alternative approaches and options when faced with delivery constraints
- Mentor and grow a multidisciplinary team of LLM-focused Data Scientists and ML Engineers.
- Drive cross-functional collaboration with Legal SMEs, Data Engineers, Product Managers, and Design.
- Establish best practices for evaluation, observability, and responsible use of generative AI.
- Oversee development of infrastructure to support continuous delivery and monitoring of LLM systems in production environments.
- Advanced degree (Master''s or PhD) in Data Science, Computer Science, Statistics, or a related field strongly preferred, or equivalent practical experience
- Bachelor''s degree in a relevant field with significant applied experience in data science, machine learning, or AI