Sr. Manager Data Science - Gen AI and Content Systems

About the role

We are seeking a hands-on Senior Manager of Data Science to lead a high-impact team in developing the strategy, standards, and execution of AI across our content ecosystem. You will lead a team that embeds machine learning and generative AI directly into production systems operating at scale.

Responsibilities

• 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
• 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

Qualifications

• 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
• 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, and having the ability to select the right tools and technologies to solve business problems.

Technical proficiency includes:

• 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
• 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 GCP
• Familiarity with AI Coding Tools (e.g. GitHub Copilot, Claude Code, OpenAI Codex)

Preferred background includes:

• 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