VP, Data Science

What the market looks like

We've tracked 600+ senior-level data science postings across the US in the last six months, with the heaviest concentration in California, New York, and Washington. Technology, financial services, and professional services firms dominate hiring for this level, with median compensation landing around $240,000 and top-quartile packages reaching $470,000+. The strongest candidates bring deep expertise in building and scaling data science organizations—they've shipped production models, led cross-functional teams through complex analytics transformations, and can operate comfortably across both technical strategy and business impact.

Job responsibilities

• Own the data science strategy and roadmap aligned with corporate objectives; define technical priorities and investment areas
• Build and scale a high-performing data science team, including hiring, mentoring, and establishing engineering practices and code standards
• Drive end-to-end delivery of analytics and machine learning initiatives from problem definition through production deployment and monitoring
• Partner with business leaders and product teams to translate strategic priorities into quantifiable data science projects and measurable outcomes
• Establish governance, data quality, and ML operations frameworks that enable the organization to move fast while managing risk
• Lead budget planning, resource allocation, and vendor evaluation for data infrastructure and ML tooling
• Communicate data science progress, insights, and recommendations to executive stakeholders and boards

Candidate requirements

• 10+ years of experience in data science, machine learning, or analytics, with at least 5 years in a leadership role managing teams and technical strategy
• Proven track record shipping production machine learning systems and scaling data science organizations from the ground up
• Strong foundation in statistical modeling, experimentation, and causal inference—able to design and interpret rigorous analyses
• Experience with data infrastructure, cloud platforms (AWS, GCP, Azure), and MLOps tooling; fluency in at least one programming language (Python preferred)
• Demonstrated ability to influence cross-functional stakeholders, translate business problems into technical requirements, and communicate impact to non-technical audiences
• Comfort operating in fast-moving or early-stage environments; track record of defining processes and team structures where they don't yet exist