Head of Data & AI

About Burq

Burq started with an ambitious mission: to turn the complex process of offering delivery into a simple, turnkey solution. It’s a big mission, and now we want you to join us in making it even bigger. We’re proud to be recognized as one of Fast Company’s Best Workplaces for Innovators and a 2025 Inc. Magazine Power Partner, awards that highlight how we’re redefining the future of logistics while empowering our partners to grow. Backed by leading Silicon Valley investors like Village Global, the fund whose investors include Bill Gates, Jeff Bezos, Mark Zuckerberg, Reid Hoffman, and Sara Blakely, we’ve built a world-class team across the globe. We operate at scale but remain small enough for every person to have a massive impact.

The Role

We're looking for a Head of Data & AI to build the intelligence that powers Burq. You'll build a world-class data-and-ML platform: the systems that turn data into models which make every delivery decision smarter and agents that automate the work the industry still does by hand. You'll set the strategy, make the hard architecture and modeling calls, ship models into production that move the business, and build the team to do it at scale. This is a player-coach role. You'll lead and grow the data and ML team while staying deeply hands-on — making architecture calls, reviewing engineering and modeling work, and ensuring the platform serves both product and business goals. You'll partner closely with the Head of Engineering, and with product and AI leadership on how intelligence shows up in the product.

What You'll Do

Data Platform

• Own the full data platform — the warehouse, streaming and batch ingestion, the semantic layer, and BI infrastructure.
• Define and enforce data governance — access management, quality standards, and a well-modeled, trustworthy 'gold' layer.
• Make the warehouse the source of truth for operational, product, and financial analytics.

Intelligence & Machine Learning

• Build the models that power delivery intelligence — forecasting, prediction, optimization, matching/allocation, and reliability scoring that turn our data into better real-time decisions.
• Stand up the ML systems that make this real and reliable: feature pipelines, training, serving, evaluation, and drift monitoring.
• Design the learning loop — instrument decisions and outcomes so models continuously improve.
• Drive the AI agent initiative to production, partnering with product and engineering on the surrounding workflows.

Team & Stakeholders

• Manage and grow the data and ML team (starting from a small core) — hire data engineers, analytics engineers, and data scientists/ML engineers as the platform matures.
• Establish a clear intake process for ad hoc data requests so the team works from a roadmap, not in reactive mode.
• Partner with Sales, Customer Success, and Finance on data-driven analyses and decisions.