Chief Data Officer

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What The Market Looks Like
We've tracked 100+ executive-level data science postings in the US over the last six months, with hiring concentrated in technology, financial services, and professional services hubs like New York, Seattle, and California. Compensation for Chief Data Officer roles typically ranges from $210K to $530K annually, with variation tied to organization size, data maturity, and AI transformation scope. The strongest candidates bring a track record of building and scaling data teams from the ground up, translating business strategy into measurable data initiatives, and shepherding organizations through the technical and cultural shifts required to make data and AI work operationally.
Job Responsibilities
Own the end-to-end data and analytics strategy—from data infrastructure and governance to applied AI/ML initiatives—and align it directly to business outcomes and revenue impact
Build and lead a high-performing data science, engineering, and analytics team; hire, mentor, and retain top talent while fostering a culture of experimentation and rigor
Drive adoption of data-driven decision-making across the organization; partner with business leaders to identify high-leverage use cases and translate them into models, dashboards, and operational tools
Establish data governance, quality standards, and security frameworks that scale with organizational growth and comply with regulatory requirements
Lead the evaluation and integration of new data platforms, tools, and technologies; own the business case and ROI for technology investments
Partner with the CTO, CFO, and executive leadership to communicate data maturity, roadmap progress, and resource needs in business and technical terms
Identify and mitigate data, model, and AI risk; ensure responsible and ethical use of data and machine learning across the enterprise
Candidate Requirements
10+ years of experience in data science, data engineering, analytics, or related technical leadership roles; minimum 5 years in a leadership capacity managing teams of 5+
Proven track record building data platforms, ML systems, or analytics infrastructure at scale; hands-on fluency with SQL, Python, and modern data stack tools (cloud data warehousing, ETL, orchestration)
Deep experience translating business problems into data solutions and shipping models or analytics that drove measurable business value (revenue, cost, efficiency, risk reduction)
Strong executive communication skills: ability to articulate technical concepts to non-technical stakeholders and influence strategy and budget allocation at the C-level
Demonstrated ability to navigate organizational change, build cross-functional alignment, and embed data culture in non-data-native organizations
Familiarity with AI/ML governance, data privacy, and regulatory requirements (GDPR, SOX, HIPAA, or equivalent domain-specific standards)

Work arrangement
Yes