Head of Data & AI

Burq, Inc.

1d•Remote

About The Position

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.

Requirements

8+ years in data/ML, including 2+ years in a leadership or staff-level role owning a data or ML platform.
Player-coach — you lead a team and still go deep in the technical work: architecture, modeling, and code review.
Strong on both halves of the role:
Data platform — modern data warehouse design (e.g., Snowflake/BigQuery), streaming + batch ingestion, dbt or equivalent, performance and cost optimization, governance.
Machine learning — you've built and shipped production models (forecasting, optimization, ranking/matching, or prediction) with real business impact, plus the ML systems around them (feature pipelines, serving, monitoring/MLOps).
Experience building data and ML products, not just pipelines — you understand how data and models serve product and business goals.
Strong stakeholder management — you can say no to an ad hoc request with a good reason and a better alternative.
Experience at a high-growth startup or scale-up where the platform was built, not inherited.

Nice To Haves

Familiarity with modern AI/LLM and agentic data work is a strong plus.

Responsibilities

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.
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.
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.