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Data Analytics AI Engineer

Fireblocks - New York

Posted Jun 1, 2026

Benefits

Parental leave
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Non-birth-parent leave
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Family-building benefits
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  • Adoption assistance: Not verified
  • Surrogacy assistance: Not verified
Mental health support
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Relocation assistance
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Childcare support
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Learning budget
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Salary
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401(k) match
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Market context

Median wage (BLS OEWS)
$111,944 national median
Projected growth (BLS Employment Projections)
+13.7% - Much faster than average

61% above the BLS national median for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type
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Weekend work
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Application

Cover letter
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Assessment
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Deadline
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Where they hire

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About this role

Data Analytics AI Engineer New York The world of digital assets is accelerating in speed, magnitude, and complexity, opening the door to new ways for leveraging the blockchain. Fireblocks' platform and network provide the simplest and most secure way for companies to work with digital assets and it trusted by some of the largest financial institutions, banks, globally-recognized brands, and Web3 companies in the world, including BNY Mellon, BNP Paribas, ANZ Bank, Revolut, and thousands more. About the Team Join our global Data team, which partners closely with Product, Engineering, and GTM stakeholders to power data-driven decision making and AI-driven products across Fireblocks. The team has evolved from providing deep analysis to becoming enablers, bringing the right data and AI tools so that Sales, Marketing, and Customer Operations can run analysis and forecasting. We operate in a fast-paced, high-growth environment where much of what we build is new, and we figure things out as we go. What You'll Do - Design and build LLM-powered systems and data agents that directly improve internal capabilities, enabling GTM, Product, and Engineering teams to make faster, smarter, data-driven decisions at scale. - Analyze large-scale datasets to surface strategic insights across the customer lifecycle and revenue funnel; define and track critical KPIs; and translate findings into actionable product and business recommendations. - Evaluate and quantify the business impact of AI initiatives through rigorous experimentation, benchmarking, and model evaluation to continuously optimize LLM performance. - Continuously monitor, refine, and evolve AI models and solutions, iterating on

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