Vice President, Data Science

Clarivate•Alexandria, VA

4d•$240,000 - $300,000

About The Position

The Vice President, Data Science sets the vision for Clarivate’s Data Science & AI organization and translates advances in machine learning, Generative AI, and Agentic AI into scalable products, improved operations, and measurable business value. The role provides executive leadership to a global, multidisciplinary organization, guiding work from AI strategy through production-grade solutions and supporting the future of the company’s Intellectual Property business. The position defines and executes enterprise data science and AI strategy, creates a multi-year roadmap, and leads the design and implementation of AI capabilities across the business. It oversees predictive modeling, advanced analytics, Generative AI, Agentic AI, model lifecycle management, responsible AI, governance, and cost optimization. The Vice President builds and develops a global organization and partners with cross-functional and executive stakeholders to ensure AI work advances business priorities and delivers measurable outcomes. The Data Science & AI organization includes Data Scientists, Applied Scientists, ML Engineers, MLOps Engineers, and specialists focused on customer insight, workflow automation, product intelligence, and strategic decision-making. The team works closely with Product, Engineering, Commercial, Operations, Legal, and Customer Success. The position primarily works full-time business hours in the employee’s time zone, with flexibility to support global business needs. The expected US base salary is $240,000–$300,000 per year, and the role is eligible for bonus earnings. Individual pay depends on experience, education, skills, ability, expertise, and relevant factors.

Requirements

Bachelor’s or master’s degree in Computer Science, Data Science, AI, Statistics, Mathematics, Engineering, Economics, or a related quantitative field.
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 delivery of production-grade AI/ML systems and data products at enterprise scale.
At least 7 years of experience in AI and machine learning 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 involving 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 involving 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 involving 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 LLM cost management, model routing, GPU and inference optimization, hybrid model strategies, FinOps, and AI ROI measurement.
At least 7 years of experience presenting AI strategy and outcomes to executives, customers, boards, investors, or industry forums.

Nice To Haves

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.

Responsibilities

Define and execute the enterprise data science and AI vision, aligning investment and delivery with business strategy, product innovation, and long-term growth.
Differentiate 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, lead, and develop a global Data Science & AI organization, fostering technical excellence, talent depth, innovation, and accountability for business impact.
Partner with Product, Engineering, Commercial, Operations, Legal, Customer Success, and executive stakeholders to align AI capabilities with business priorities and communicate technical strategy.
Establish KPIs for AI adoption, model performance, ROI, productivity, customer impact, cost efficiency, and delivery of high-value initiatives.