Director, Data Science & AI Engineering
$290,000–$440,000 year
On-site · New York City, New York, United States or San Francisco, California, United States
Job Summary
Direct, lead, and scale the firm's enterprise Data Science & AI Engineering function, building a multidisciplinary team of Data Scientists, Data Analysts, and MLOps/AI Engineers to deliver production-ready AI solutions. Oversee design, implementation, and optimization of LLM-powered applications, RAG systems, AI agents, and advanced analytics that provide actionable matter, financial, and operational insights to firm leadership. Establish scalable architecture, governance, evaluation standards, security practices, and operational frameworks for a highly regulated professional services environment. Collaborate with Knowledge Management & Innovation and other stakeholders to translate legal and operational needs into practical AI-driven solutions that improve efficiency, decision-making, and service delivery. Lead internal AI platforms and solutions, ensure integrations with matter management, document management, timekeeping, and BI tools; drive data modeling, warehousing/lakehouse strategy, and dashboards; implement governance, security, and compliance controls; and guide strategic make-vs-buy decisions and vendor partnerships.
Required Qualifications
- Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Data Science, or a related quantitative field, or equivalent combination of education and relevant professional experience
- 10+ years of progressive experience in data science, machine learning, AI engineering, or a related technical discipline
- Demonstrated leadership experience building and managing high-performing technical teams
- Proven success designing, deploying, and supporting production AI/ML solutions including LLM applications, RAG systems, and agent-based workflows
- Strong technical foundation to evaluate system architecture, engineering approaches, model performance, and technical design decisions
- Experience establishing and supporting MLOps/LLMOps practices including cloud infrastructure, deployment automation, monitoring, security, and governance
- Experience leading enterprise analytics and BI initiatives with executive-facing dashboards and reporting
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