Head of Analytics

About SuperAGI:

SuperAGI is pioneering the future of Artificial General Intelligence with groundbreaking research and innovative AI products. Our mission is to transform the future of applications through intelligent, autonomous solutions that drive unparalleled efficiency and growth. We are building a world where AI and human intelligence collaborate seamlessly to achieve extraordinary outcomes. If you are passionate about AI and eager to be part of a team that is shaping the future, SuperAGI is the place for you.

Position Overview:

We’re looking for a Head of Analytics to lead our data analytics function and drive strategic insights across the business. This is a high-impact leadership role responsible for building and scaling a data-driven culture, enabling informed decision-making, and aligning analytics with business goals.

You’ll work cross-functionally with product, marketing, sales, finance, and engineering to deliver actionable insights, improve operational efficiency, and support long-term growth.

Key Responsibilities:

• Develop and execute performance marketing strategies across various digital channels to drive customer acquisition and revenue growth.
• Build, lead, and mentor a high-performing analytics team, including data analysts, BI engineers, and data scientists
• Define team structure, roles, and responsibilities to align with business priorities
• Lead the creation and management of paid campaigns across multiple platforms, ensuring alignment with business objectives and key performance indicators (KPIs).
• Work closely with product, marketing, sales, finance, and operations to align analytics with business needs.
• Analyze campaign performance and continuously optimize bids, targeting, creatives, and messaging to ensure maximum ROI.
• Monitor and manage daily, weekly, and monthly performance reports, ensuring campaigns stay on track and meet performance goals.
• Collaborate with engineering and data teams to ensure data quality, consistency, and scalability of analytics infrastructure (e.g., data warehouse, ETL pipelines, BI tools).
• Conduct A/B testing and experimentation to optimize landing pages, ad creatives, and overall campaign performance.
• Build and manage the marketing budget, tracking spend against performance goals and optimizing for cost-efficiency.
• Keep up to date with industry trends, new advertising technologies, and best practices in digital marketing.
• Drive growth initiatives through retargeting, CRM-based campaigns, and advanced segmentation strategies.
• B2B SaaS experience is mandatory, with a deep understanding of SaaS sales cycles, customer lifetime value (CLV), and the need for ongoing user retention.

What We’re Looking For :

• 8–10 years of experience in analytics, business intelligence, or data science , including 4+ years leading analytics or data teams within a B2B SaaS environment .
• Proven track record of using data to drive business outcomes , influence strategic decisions, and deliver measurable impact across key company metrics.
• Demonstrated experience in developing and executing data strategies , defining KPIs, and building scalable analytics solutions that support cross-functional business goals.
• Expertise in data visualization and BI tools such as Looker, Tableau, or Mode , with the ability to design executive-ready dashboards and enable self-serve analytics .
• Advanced proficiency in SQL and strong understanding of data modeling concepts , including star/snowflake schemas, dimensional modeling, and ETL/ELT best practices.
• Experience working with modern data warehouses like Snowflake, BigQuery, or Redshift , and collaborating with data engineering teams to ensure data quality and accessibility.
• Skilled at performing deep-dive analyses , building forecasting models , conducting cohort analysis , and leading A/B testing or experimentation frameworks .
• Strong communication skills with the ability to translate complex data into actionable insights and deliver strategic recommendations to executive stakeholders .
• Comfortable working cross-functionally with teams such as product, finance, operations, and customer success to support data-informed decision making at scale.