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As a Data Scientist at Mercury focused on Financial & Accounting Workflows, you will partner with product and engineering teams building tools that help founders, bookkeepers, and finance teams manage their businesses. Your work will span core accounting needs and broader financial workflows. You’ll develop deep expertise in how these workflows can be automated, streamlined, and made more insightful through data. Your impact will shape how customers adopt, engage with, and find value in Mercury’s financial tools.
Job Responsibility:
Partner with Financial & Accounting Workflow stakeholders to define success metrics for new product initiatives and ensure teams have the right measurements in place
Collaborate with product and engineering to design and analyze experiments that guide product decisions and validate impact
Contribute to launches of new products like Books by helping scope success criteria, tracking adoption, and analyzing performance post-launch
Work closely with product teams to surface insights that inform iteration and help identify opportunities for deeper engagement or new product lines
Partner with engineers to prototype and apply ML/AI solutions that streamline financial workflows
Build dashboards, data pipelines, and reporting that enable self-serve analytics for product and business partners
Requirements:
Have 3–5 years of experience in product analytics or data science, ideally with exposure to financial workflows, accounting, or B2B SaaS products
Be fluent in SQL and proficient in a statistical programming language (e.g., Python)
Have experience designing and analyzing A/B tests, and applying causal inference methods
Be comfortable building data pipelines, dashboards, and reports using modern data stack tools such as Snowflake, dbt, and Omni or Metabase
Have experience with ERP/accounting systems (QuickBooks, Xero, NetSuite) or accounting workflows
Experience applying ML or GenAI to real-world product problems
Be organized and communicative, able to manage multiple priorities and collaborate with stakeholders at different levels of technical fluency
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