Heads up: this is not a live role. Axial Search recruits for clients across North America — we post representative roles like this so candidates can raise their hand before we have a specific mandate. Apply today and we'll reach out the next time a job like this comes up.

The world of work is changing faster than ever before — but the truth is, technology doesn't transform companies. People do. We build long-term relationships with the leaders driving that change across every industry vertical.

Visit our website to learn more about our process and to find free tools to support your job search — including our live job market dashboard with salary, skills, and hiring-trend data from thousands of AI transformation roles.

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.

Typical 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

Typical 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

VP, Data Science