VP, Data Science
United States · Remote · Permanent
Heads up: this posting is for future opportunities rather than one specific open role. If you apply, we'll add you to our candidate network and may reach out when relevant roles come up.
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What the market looks like
We've tracked 100+ senior-level data science postings across the US in the last six months, with strongest concentration in California, New York, and Washington. Technology, financial services, and professional services lead demand. Compensation for this cohort ranges from $180K to $310K annually. The strongest candidates bring a track record of building and scaling data science teams, translating business problems into analytical strategies, and shipping models that measurably impact revenue or operations—paired with the judgment to know when ML solves the problem and when simpler approaches work better.
Job responsibilities
Own the data science strategy and roadmap, aligned with broader business objectives and AI/ML transformation initiatives
Build and lead a high-performing data science team, including hiring, coaching, and managing performance across analytics, modeling, and ML engineering roles
Partner with product, engineering, and business leadership to identify high-impact opportunities and translate them into analytical and modeling projects
Drive the end-to-end delivery of data science initiatives—from problem definition and experimentation through production deployment and measurement
Establish data science best practices, tooling, and infrastructure to accelerate time-to-insight and model velocity
Communicate findings and recommendations to non-technical stakeholders, making the case for investment in data-driven decision-making
Stay current on advances in machine learning, statistical methods, and AI applications relevant to your industry and advocate for adopting promising new approaches
Candidate requirements
8+ years of experience in data science, analytics, or machine learning, with at least 3–4 years leading or managing a data science team
Proven ability to define and execute a data science strategy that drives measurable business outcomes
Deep technical fluency in statistical modeling, machine learning, and programming (Python, R, SQL); you can code-review team work and spot technical debt
Experience building and deploying models to production, with understanding of the full ML lifecycle from ideation through monitoring and iteration
Strong communication skills and ability to explain complex analytical concepts to product managers, executives, and non-technical audiences
Track record of recruiting, developing, and retaining strong technical talent