VP, Data Science

The VP, Data Science is accountable for establishing and scaling deep analytics and predictive intelligence capabilities across the enterprise to increase both the operational effectiveness and efficiencies of our internal functions. In this role, you will define where machine learning creates value, build the team and infrastructure to deliver it, and embed data science into strategic organization-wide decision‑making. This is a foundational role for someone who has built data science functions before and understands how to translate technical capabilities into business impact across end‑to‑end customer lifecycle.

• Build and lead a data science organization from the ground up, setting vision, operating model, and team structure.
• Recruit, develop, and retain top‑tier talent across data science, engineering, and related technical roles at multiple experience levels.
• Create a culture of experimentation, analytical rigor, and strong business partnership across Rev Ops, Marketing, Sales, Customer Success, Delivery, IT, and more.
• Identify and prioritize high‑ROI use cases spanning demand generation, conversion optimization, customer adoption, retention, pricing, forecasting, and operational efficiency.
• Translate business opportunities into actionable data science projects (e.g., win/loss analytics, churn root‑cause analysis, AE/BDR performance insights).
• Deploy enterprise‑scale ML systems with robust monitoring, governance, and MLOps infrastructure for rapid, reliable model deployment and decision dashboards.
• Architect scalable ML/AI infrastructure for batch and real‑time inference, adopt emerging technologies (LLMs, AutoML, Agentic AI), and set standards for model lifecycle management.
• Influence C‑suite decisions with predictive insights, drive organizational data literacy, and represent the company externally through thought leadership.

• Experience as a leader in data science, analytics, machine learning, GenAI or related quantitative fields, ideally having built and scaled high‑performing teams from scratch / early stages through to maturity (preferably having proficiency in building GenAI teams).
• Experience deploying production ML systems that delivered measurable business outcomes (revenue growth, cost reduction, efficiency gains).
• Experience operating in enterprise software, SaaS, ERP, or B2B technology environments.
• Experience applying GenAI and/or Agentic AI to real‑world enterprise use cases.
• Deep expertise in machine learning, statistical modeling, optimization, and causal inference, with hands‑on experience with modern ML tech stacks (Python, Spark, Tensor Flow/PyTorch, cloud platforms), as well as understanding of MLOps practices, model lifecycle management, and production ML infrastructure.
• Experience designing and deploying GenAI solutions, including LLM fine‑tuning, prompt engineering, and retrieval‑augmented generation (RAG), having familiarity with GenAI/LLM applications in enterprise contexts, including Agentic AI frameworks and orchestration tools (e.g., Lang Chain, Auto Gen, Semantic Kernel).
• Experience influencing C‑suite and driving organizational change, as well as having had experience managing P&L impact and presenting ROI of analytics investments to senior leadership.
• Full‑Stack Data Ecosystem Experience, having fluency across the data stack, includes modern platforms (e.g., Snowflake), orchestration tools (e.g., dbt), and cloud‑native architectures, plus strong ML/AI proficiency for scalable solutions.
• Value Creation Mindset, prioritize innovation and business value creation when shaping data and analytics strategies, while building in rigor and scalability in parallel.
• Experience in Holistic Analytics Design, having the ability to assess a business problem, identify the insights required, determine the necessary data, and design end‑to‑end analytics solutions.

• Experience with AWS AI tooling and services, e.g., Bedrock, Sage Maker.
• Background in customer analytics (Demand Gen funnel analytics, product adoption analytics, churn prediction, lifetime value, segmentation, etc.).
• Publications, patents, or…