VP, Data Science

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What The Market Looks Like
We've tracked 100+ senior-level data science postings in the United States over the last six months, with concentrated hiring in California, New York, and Washington. Organizations across technology, financial services, professional services, and manufacturing are building or scaling data science teams, and compensation for experienced VPs typically ranges from $180K to $350K. The strongest candidates in this market bring deep technical credibility in machine learning and statistical modeling, proven ability to lead and develop data science teams, experience translating complex analyses into business strategy, and a track record shipping production systems that drive measurable ROI.
Job Responsibilities
Own the end-to-end data science strategy and roadmap, partnering with product, engineering, and business leadership to identify high-impact use cases and prioritize delivery
Build and lead a high-performing data science team, hiring talent, setting technical standards, and fostering a culture of rigor and experimentation
Drive the development and deployment of machine learning models that solve real business problems, from ideation through production and monitoring
Translate complex analytical findings into clear insights and recommendations for executive stakeholders, communicating trade-offs and business impact
Establish best practices for data governance, model validation, and measurement frameworks across the organization
Partner with data engineering and infrastructure teams to ensure scalable pipelines, reproducible workflows, and reliable model serving
Evaluate emerging techniques and tools in machine learning and AI, balancing innovation with pragmatic delivery timelines
Candidate Requirements
7+ years of hands-on experience in data science, machine learning, or quantitative analysis, with demonstrated expertise in statistical modeling and ML techniques
3+ years leading and managing data science or analytics teams, with a track record of growing talent and shipping impactful work
Proven ability to take models from concept to production, including experience with model deployment, monitoring, and iteration in real-world settings
Strong communicator who can translate technical complexity for non-technical stakeholders and align data science work with business objectives
Experience working cross-functionally with product, engineering, and business teams to scope and prioritize data science initiatives
Fluency in Python or R and familiarity with modern ML tools and infrastructure (cloud platforms, containerization, workflow orchestration)

Work arrangement
Yes