[Hiring] VP of AI, ML and Data Strategy @Shipium

Role Description

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.
• 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.

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.
• ML frameworks (e.g., scikit-learn, PyTorch, XGBoost) and applied experience with production predictive modeling.
• 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).

Requirements

• Set the multi-year strategy for ML, data, and BI, grounded in customer signal, competitive intelligence, platform reality, and financial constraint.
• Connect ML, data, and BI investments into a coherent narrative about how Shipium turns operational data into customer outcomes.
• 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.
• 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.
• Set and enforce engineering and analytical standards — code quality, testing, monitoring, documentation, and model-evaluation discipline.
• 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.

Benefits

• Full medical, dental & vision coverage (with 65% coverage for dependents).
• Optional life insurance and long-term disability coverage.
• 401(k) retirement plan.
• Fully remote work-from-home options in 28 states.
• 8 paid weeks of parental leave.
• Paid holidays annually.
• Self-managed vacation time, sick & safety leave, and volunteer time off.