Role Description 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. Qualifications - 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. - Familiarity with modern AI/LLM and agentic data work is a strong plus. - 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. Location - This is a fully remote role, open to candidates based in the United States and Canada, with a few in-person offsites throughout the year. How We Work - We're remote with a few in-person offsites throughout the year. - We move quickly, default to writing, and ship early to real customers before rolling things out broadly. - AI is part of how we work every day, not a separate initiative — we expect people to experiment with new tools, share what they learn, and continuously rethink how great products get built. - We care more about speed, ownership, and iteration than process for the sake of process. Benefits - Competitive salary, stock options, and performance-based bonuses. - Fully remote. - Comprehensive medical, vision, and dental insurance. Company Description At Burq, we value diversity. We are an equal opportunity employer: we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

• Enhancing Models: Continuously improving existing models by incorporating new features, fine-tuning parameters, and utilizing additional data. • Algorithm Development: Collaborating with our Machine Learning Engineering team to develop and deploy scalable supervised and unsupervised learning algorithms. • Data Exploration: Identifying and exploring new data sources while researching innovative ways to leverage both new and existing data effectively. • Solution Research: Investigating new Machine Learning solutions to optimize every step of our value chain. • A/B Testing: Regularly conducting and evaluating A/B tests to drive insights and refine our strategies.

• Ensure operational efficiency and excellence by leading and/or assisting in the creation and documentation of new or revised department procedures. • Recommend appropriate departmental objectives that support the company strategy and objectives and create solutions to address business and operational challenges. • Lead working groups and/or committees to set goals and execute on implementation to address process or procedural gaps. • Partner with internal and external stakeholders to optimize Data Management technology, processes, and standards. • Participate in project and department budget development and responsible for effective management of budgets for assigned projects and staff. • Participate in the evaluation and selection of data system vendors to enhance Data Management processes, e.g. EDC, IRT, ePRO, eConsent. • Contribute to the financial success of the department and company by effectively managing financial aspects of assigned programs, including but not limited to Work Orders, Change Orders, study forecasting and budgeting. • Identify and quantify resource requirements for ongoing and future projects; Interview, hire, onboard and train new staff as appropriate. • Supervise, mentor, provide leadership and maintain positive team morale; facilitates conflict resolution. • Oversee development of direct reports by setting and tracking individual objectives and key results to support department and company strategies and objectives. • Evaluate training needs and conduct performance reviews for assigned staff. • Set priorities and oversee activities for assigned staff. • Ensure Data Management deliverables for projects assigned meet/exceed project/study team expectations regarding quality, time, and cost. • Serve as the subject matter expert on data collection strategy and handling of study data for assigned programs and effectively liaise with programming to ensure clinical databases are designed in a standard, accurate, complete, and consistent format to produce datasets that are conducive to analysis and a possible regulatory submission. • Proactively identify data management project issues, manage risks, and alert Senior Department Management with a view to remedial action. • Interact/liaise with other departments to ensure a high level of client satisfaction through successful issue resolution and execution of projects. • May work with other departments and analysts on special projects to ensure company end to end processes and technologies are efficient and effective, work seamlessly together and deliver improved customer value. • May represent Data Management at business development, sponsor, or other professional meetings. • Assist the business development team with RFIs, RFPs and project pricing.

• Design and implement measurement and evaluation frameworks to understand the impact of Trust defense, and uncover opportunities for improvement • Collaborating closely with cross-functional partners, lead the design and analysis of experiments and quasi-experiments in environments where standard A/B testing is challenging. • Build and iterate on statistical and Bayesian models that quantify risk, estimate treatment effects, and surface measurement gaps; provide causal interpretation of signals surfaced by ML systems • Generate deep insights on the effectiveness of Trust defenses and translate them into clear strategic recommendations for product roadmap and policy decisions • Deliver robust, leadership-ready research with rigorous analysis, compelling data visualizations, and clear narratives; effectively communicate complex methodological tradeoffs to both technical and non-technical audiences across product, engineering, policy and operations • Think strategically about how to scale and evolve data science capabilities within your domain, contributing to the long-term vision for how science drives platform outcomes