Manager (Data Science with AI)

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

Technical Proficiency

• 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)

Preferred Background

• 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.

Key Responsibilities: Scope & Impact

• 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

Technical & Product Leadership:

• 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

Lead through execution and by example:

• 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

Team & Operational Excellence:

• 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.

Core Qualifications: Experience & Education

• 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