VP of AI, ML and Data Strategy
$277,000–$290,000 year
Remote · United States
Job Summary
VP of AI, Machine Learning, and Data Strategy leading the Data Science, AI, Data Engineering, and BI Engineering functions, overseeing production ML behind delivery promise, carrier selection, fulfillment optimization, real-time inference, and the data/BI platform. Shapes multi-year strategy for ML, data, and BI; drives cross-functional partnerships with Product, Engineering, Implementations, Sales, Marketing, and Finance; owns budgeting, hiring standards, and governance; leads a senior, multi-function team, and champions generative AI initiatives, tooling, and evaluation practices. Responsibilities include setting roadmaps, making build-vs-buy decisions, scaling the organization, defining engineering and analytical standards, ensuring production reliability and cost efficiency, and representing the function in executive decisions and customer conversations. Required capabilities include modern ML and data stack proficiency, experience with LLMs and GenAI, end-to-end production ML operations, and strong leadership across data science, data engineering, and BI domains.
Required Qualifications
- Experience and qualifications: 10+ years in data science, machine learning, or data engineering, with at least 5 years in a people-management or technical leadership role — including managing managers or tech leads and representing the function to executive stakeholders
- Demonstrated experience building or substantially scaling a data, ML, or AI organization — hiring senior individual contributors and leaders, standing up or maturing the operating model, and owning a function-level budget
- Strong working knowledge of the modern ML and data stack, with credible architectural and investment judgment across all of it
- LLM application development — orchestration frameworks (e.g., LangChain, LlamaIndex), prompt engineering, evaluation harnesses, RAG, and tool-using agents; familiarity with commercial and open models (e.g., OpenAI, Claude, Gemini, Llama) and their trade-offs
- B2B SaaS, supply chain, logistics, or e-commerce experience strongly preferred
- Master's degree in Computer Science, Data Science, Statistics, Mathematics, Operations Research, or a related quantitative field preferred. Equivalent industry experience considered
- Modern data platforms — data warehousing on Amazon Redshift, transformation via dbt, orchestration via Airflow, and data lakes on AWS S3
- Cloud infrastructure on AWS — including SageMaker, S3, and Bedrock — and a working sense of the cost and reliability trade-offs across managed vs. self-hosted options
- Production ML operations — CI/CD for ML, model monitoring, and observability (e.g., Datadog)
Desired Qualifications
- Demonstrated experience building or substantially scaling a data, ML, or AI organization — hiring senior individual contributors and leaders, standing up or maturing the operating model, and owning a function-level budget
- Master's degree in Computer Science, Data Science, Statistics, Mathematics, Operations Research, or a related quantitative field preferred. Equivalent industry experience considered
- LLM application development — orchestration frameworks (e.g., LangChain, LlamaIndex), prompt engineering, evaluation harnesses, RAG, and tool-using agents; familiarity with commercial and open models (e.g., OpenAI, Claude, Gemini, Llama) and their trade-offs
- Strong working knowledge of the modern ML and data stack, with credible architectural and investment judgment across all of it
- B2B SaaS, supply chain, logistics, or e-commerce experience strongly preferred
- Production ML operations — CI/CD for ML, model monitoring, and observability (e.g., Datadog)
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