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

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

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

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

Requirements

Deep expertise in the modern data stack: BigQuery, DBT, Airflow, real-time pipelines, semantic layers
Hands-on experience with:
Generative AI / LLMs (RAG, copilots, natural language interfaces)
Experimentation frameworks and causal inference
Machine learning (forecasting, optimization, recommendations)
Familiarity with: Feature stores, MLOps, vector databases, and AI orchestration frameworks
Strong understanding of data modeling, governance, and scalable architecture

Proven ability to build and lead multi-disciplinary data teams
Strong communication skills; able to translate complex data/AI concepts into business value
Highly analytical and structured thinker
Comfortable operating in fast-paced, ambiguous, high-growth environments
Collaborative, pragmatic, and outcome-driven

Head Of Data & Analytics (ai Native)