Vice President, Data Science

Shape the future of AI in Intellectual Property. As Vice President, Data Science, you will set the vision for our Data Science & AI organization and turn advances in machine learning, Generative AI, and Agentic AI into scalable products, smarter operations, and measurable business value. This is an opportunity to pair technical ambition with executive leadership—guiding a global, multidisciplinary organization from AI strategy to production-grade solutions.

If you have led enterprise-scale AI teams and brought AI solutions into production, we invite you to help define what comes next for our IP business.

• 15+ years of progressive experience in Data Science, AI, Machine Learning, Advanced Analytics, or related fields, including 8+ years leading enterprise-scale global teams.
• Experience managing Data Scientists, Applied Scientists, AI Researchers, ML Engineers, Data Engineers, and MLOps professionals.
• At least 7 years of experience defining AI strategy and leading the delivery of production-grade AI/ML systems and data products at enterprise scale.
• At least 7 years of experience in AI and machine learning, with experience spanning deep learning, NLP, RAG, LLMs, Generative AI, Agentic AI, MLOps, and responsible AI practices.
• At least 7 years of experience presenting AI or machine learning technical strategy to executive stakeholders and partnering with Product, Engineering, Commercial, Operations, Legal, and Customer Success teams.
• At least 7 years of experience developing or leading AI/ML solutions involving predictive modeling, deep learning, NLP, recommendation systems, forecasting, computer vision, graph methods, or experimentation.
• At least 7 years of experience developing or leading AI solutions, including experience with Generative AI, LLMs, SLMs, RAG, multimodal AI, knowledge graphs, prompt engineering, agentic workflows, evaluation, AI safety, and hallucination mitigation.
• At least 7 years of experience developing or leading AI/ML model development, including experience with pretraining, fine-tuning, PEFT/LoRA, quantization, distillation, explainability, benchmarking, and A/B testing.
• At least 7 years of experience in AI/ML engineering or production deployment, including experience with deployment pipelines, monitoring, feature stores, vector databases, drift detection, observability, data quality, cloud-native platforms, and Kubernetes.
• At least 7 years of experience managing AI/ML costs and measuring business value, including experience with LLM cost management, model routing, GPU and inference optimization, hybrid model strategies, Fin Ops, and AI ROI measurement.
• At least 7 years of experience presenting AI strategy and outcomes to executives, customers, boards, investors, or industry forums.
• PhD in Computer Science, Data Science, AI, Statistics, Mathematics, Engineering, Economics, or a related quantitative field.
• Experience in Intellectual Property, Legal Technology, Information Services, Scientific Research, SaaS, or Knowledge Management industries.
• Recognized AI/ML contributions through patents, publications, conference talks, or AI Centers of Excellence leadership.

Define and execute the enterprise data science and AI vision, aligning investment and delivery with business strategy, product innovation, and long-term growth. Differentiate our products, operations, and customer experiences through AI. Create a multi-year roadmap for machine learning, Generative AI, Agentic AI, and advanced analytics adoption. Lead the strategy, architecture, and implementation of Generative AI and Agentic AI solutions across the business.

Build scalable AI agents, copilots, assistants, reasoning systems, and workflow automation capabilities. Set standards for prompt engineering, context management, retrieval, orchestration, and agent evaluation. Lead predictive modeling, recommendation systems, forecasting, analytics platforms, and reusable AI capabilities. Translate business opportunities into scalable AI solutions that improve decisions, customer outcomes, and operational performance. Define the strategy for foundation model selection, fine-tuning, deployment, benchmarking, governance, lifecycle management, and cost optimization.

Evaluate responsible uses of proprietary enterprise data and open-source and commercial model ecosystems. Establish enterprise standards for responsible AI, governance, transparency, security, compliance, explainability, monitoring, and risk mitigation. Build,…