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What the market looks like
We've tracked 600+ senior-level postings in data science across the US over the last six months, with particular concentration in California, New York, and Washington. Chief Data Officer roles span technology, financial services, professional services, and healthcare, with median compensation landing around $240K and strong performers reaching $400K–$470K. The strongest candidates in this space combine deep technical credibility in data architecture and analytics with proven ability to translate data strategy into business outcomes—they've typically built or scaled data functions, shipped production systems, and navigated the organizational challenges of embedding data-driven decision-making across the enterprise.
Job responsibilities
Own the end-to-end data strategy, from infrastructure and pipeline architecture through governance, quality, and analytics—balancing technical rigor with business impact
Lead and scale the data science and engineering team, setting hiring bar, mentoring senior ICs, and fostering a culture of experimentation and accountability
Partner with C-suite and business leaders to identify high-value data initiatives, prioritize roadmap investments, and communicate data-driven insights that move the business
Drive data governance, security, and compliance frameworks that enable scale without creating bottlenecks
Integrate AI/ML capabilities into core products and operations—working cross-functionally with product, engineering, and finance to identify where automation and prediction create defensible advantage
Build and maintain the data platform: modern data stack, cloud infrastructure, tooling, and processes that enable self-service analytics and reduce time-to-insight
Establish metrics, KPIs, and dashboards that connect data work to revenue, efficiency, and strategic outcomes
Candidate requirements
10+ years in data science, data engineering, or analytics, with 5+ years in a leadership or staff-level role building and scaling data functions
Proven experience shipping production data systems—data pipelines, warehouses, lakes, or real-time analytics platforms—and managing technical debt at scale
Track record of translating data capabilities into measurable business value; you've worked cross-functionally with product, finance, and operations to drive adoption
Strong foundation in SQL, statistics, and data architecture; comfortable diving into technical details while leading teams and setting vision
Experience with cloud platforms (AWS, GCP, Azure) and modern data tooling; familiarity with ML engineering, MLOps, or AI integration a plus
Excellent communication skills—you can explain data concepts to both technical and non-technical stakeholders and navigate organizational change