Head Of Data & Analytics (ai Native)

Key Responsibilities

1. Data, 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

2. 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

3. 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

4. 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

Requirements

Experience & Domain Expertise

• 10+ years in data, analytics, and AI roles, with 3+ years in leadership
• Strong experience in ecommerce, marketplaces, martech, or digital platforms
• Proven track record building modern data platforms and scaling data teams
• Experience working in multi-country / SEA environments is a strong plus

AI & Data Expertise

• 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

Leadership & Ways of Working

• 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