Head of Data and Analytics

You will:

• Own the end-to-end data, analytics, and AI strategy
• Build a platform that shifts the company from reporting → insights → actions → autonomous optimization
• Enable both internal teams and external clients with self-serve, AI-powered decision-making tools
• Partner closely with Product, Engineering, and Business teams to embed intelligence into every workflow

This role requires someone who is equally comfortable:

• Defining data + AI strategy at the leadership level
• Designing modern data and ML architectures
• Driving real business impact through insights and automation

Key ResponsibilitiesData, Analytics & AI Strategy

• Define and execute the roadmap for an AI-native data platform aligned with Intrepid's product and business strategy
• Evolve the organization from dashboarding → proactive insights → autonomous intelligence systems
• Identify and prioritize high-impact opportunities across ML, GenAI, and agentic workflows in ecommerce, marketing, and social commerce
• Drive adoption of AI-powered self-serve analytics and copilots across business teams

Data Platform, Architecture & Governance

• Own the design and evolution of the modern data stack, including:
  • Data warehouse/lakehouse (BigQuery, StarRocks)
  • Data pipelines (Airflow, DBT)
  • Semantic layer (Cube.dev)
  • BI tools (Looker Studio, Superset)
• Extend the platform to support:
  • Real-time and streaming data use cases for social commerce
  • Vector databases, MCP, and LLM integration layers
• Establish strong foundations for data governance, privacy, security, quality, lineage, and cataloging
• Ensure platform scalability, reliability, and cost efficiency

AI, Machine Learning & Intelligent Systems

• Lead the development and deployment of:
  • Anomaly detection and automated insight systems
  • Generative AI use cases (copilots, automated reporting, content insights)
  • Recommendation systems (product, pricing, targeting)
  • Predictive models (forecasting, ROAS optimization, churn, inventory planning)
• Drive adoption of LLM-powered analytics (NL2SQL, conversational BI, insight generation)
• Build foundations for agentic AI systems that can autonomously analyze, recommend, and act

Business Impact, Self-Serve & Team Leadership

• Transition from pull-based dashboards → push-based, actionable insights
• Enable business teams with:
  • Proactive alerts, recommendations, and decision support tools
  • Scenario simulation and planning capabilities
• Build AI-powered self-serve analytics experiences, reducing dependency on central teams
• Drive semantic layer adoption and metrics standardization across the organization
• Define and track impact metrics (e.g., GMV uplift, marketing efficiency, cost reduction, automation rates)
• Build and lead a high-performing data organization (Data Engineering, Analytics Engineering, Data Science, BI)
• Foster a culture of ownership, experimentation, and AI-first thinking, partnering closely with tech and business functions