VP of AI, ML and Data Strategy

About Shipium Shipium builds technical infrastructure for complex supply chains. Modern operators turn to Shipium when they want to turn their supply chain into a strategic value driver. Shipium's platform provides cloud infrastructure and leading AI capabilities that optimize costs and scale automation. We’re building tech that connects previously fragmented systems and automates complex supply chain decisions to deliver speed and value across operations. Founded in 2019 by supply chain technology experts from Amazon and Zulily, the company is on a mission to help every eCommerce company provide its customers a great delivery experience while simultaneously reducing their costs to fulfill orders. VP AI, Machine Learning, and Data Strategy (Remote)* About the role This leader heads Shipium's Data Science, AI, Data Engineering, and Business Intelligence Engineering functions — and the production systems they own run the platform. The team owns the production machine learning behind delivery promise, carrier selection, fulfillment optimization, and simulation, including real-time inference at platform scale. It owns the data engineering platform that powers analytics and modeling — the warehouse, transformation pipelines, and orchestration that downstream systems depend on. And it owns Business Intelligence Engineering, which delivers Orca Analytics and the internal and customer-facing reporting Shipium runs on. The function is also a foundational partner to Shipium's broader Generative AI work, providing the data, LLM, and agent infrastructure, evaluation practices, and proprietary model integrations that other product and engineering teams build on. The VP balances executive-level strategy with substantive technical engagement — an architecture review, a model post-mortem, or hands-on contribution to the codebase when the work demands it. Success rests on technical credibility, an exacting hiring bar, and sound judgment across the organization. The role works across Product, Engineering, Implementations, Sales, Marketing, and Finance to turn these capabilities into customer outcomes. What you'll do Strategy and vision Set the multi-year strategy for ML, data, and BI, grounded in customer signal, competitive intelligence, platform reality, and financial constraint — and position the function's Generative AI work as a foundational capability other teams build on. Connect ML, data, and BI investments into a coherent narrative about how Shipium turns operational data into customer outcomes, and communicate that narrative clearly to the executive team. Make build-vs-buy and managed-vs-self-hosted calls across the ML and data stack, with a clear rationale documented for the team and for finance. Run quarterly and annual planning for the function, operating the roadmap as a structured artifact reconcilable with Jira and stakeholder communications. Team leadership and development Build and scale the Data Science / AI, Data Engineering, and BI Engineering organization — owning org design, headcount strategy, and the leveling that takes the function to its next stage of maturity. Manage managers and tech leads, not only individual contributors; grow the internal leadership bench and create the management layer the org needs as it scales. Own the technical hiring bar across all three sub-functions. Run senior and leadership recruiting personally; design the interview loops and calibration that protect the bar as hiring volume grows. Set and enforce engineering and analytical standards — code quality, testing, monitoring, documentation, and model-evaluation discipline — through review and operating cadence rather than exhortation. Run performance management, career development, and retention; define leveling and progression for data science, ML, data engineering, and BI engineering tracks. Foster a culture of technical rigor, written communication, and cross-team ownership, and act as its visible standard-bearer. Cross-functional partners