Data Scientist

Finance Ai And Automation Leader

This is a hybrid role based in RTP. Members of the Finance organization at select locations will generally be expected to follow a hybrid work model, which includes two days of in-office attendance each week, with limited exceptions.

Networking and Collaboration Finance supports some of Cisco's largest businesses, including Campus Networking, Data Center Networking, Internet Infrastructure, Compute, Collaboration, and ThousandEyes. Our AI and Automation team builds the shared foundation that lets hundreds of finance professionals use AI on their real work: a data layer that refreshes every morning, AI agents that answer questions from live data in plain English, and an internal marketplace that puts those tools in everyone's hands. We are a small team that builds things people use every day.

Your Impact

You will own our Data Layer, the Power BI semantic model behind our finance AI tools, and use it as the base for what we build next. The model pulls from SAP HANA, Snowflake, and SharePoint and holds hundreds of millions of records across bookings, pipeline, forecast, units and ASP, annual order value, and OpEx, with revenue and gross margin coming next. Finance teams query it every day through agents in GitHub Copilot and Claude, so your work shows up directly in the numbers leaders use. You will report to the AI and Automation Leader for Networking and Collaboration Finance.

Own and extend the semantic model, including table relationships, DAX measures, dataflows, refresh schedules, and role-based access, and keep it maintainable with Git, clear documentation, and controls that follow Cisco data-handling and AI policies.

Bring new data domains into the model, such as revenue, gross margin, and COGS from Snowflake, tie each one out to official reporting, and build automated reconciliation checks that catch broken refreshes and mismatched numbers before finance teams see them.

Improve how AI agents read the model through measure descriptions, model metadata, test question sets, and the MCP connectors that let agents query live data.

Design and ship new tools with coding agents, such as Python automations, machine learning models, agent skills, custom dataflows for individual teams, and connectors to new systems.

Partner with FP&A teams and our AI Champions network to turn business questions into data requirements and explain what the data can and cannot answer.

Minimum Qualifications

Bachelor's degree in data science, statistics, computer science, engineering, economics, finance, or a related field and 3+ years of experience in data science, analytics engineering, or business intelligence, or a Master's degree in a quantitative field and 1+ years of experience.

3+ years of proven ability building and maintaining Power BI semantic models, including star-schema data modeling, DAX, Power Query (M), and dataflows in the Power BI Service or Microsoft Fabric.

3+ years of proven ability writing SQL against large datasets, including Snowflake, using joins, window functions, and CTEs.

3+ years of proven experience using Python for data processing, API calls, and automation, with code managed in Git.

1+ year of experience using coding agents such as Claude Code, OpenAI Codex, or GitHub Copilot to build and ship working tools, including reviewing, testing, and correcting agent-written code.

Preferred Qualifications

Experience with finance or sales data such as bookings, revenue, pipeline, or OpEx, including fiscal calendars, period-over-period reporting, and SAP HANA as a source system.

Experience with Power BI and Fabric REST APIs, XMLA endpoints, deployment pipelines or CI/CD, and row-level security for confidential financial data.

Experience building MCP servers, agent skills, or applications on LLM APIs, and evaluating the accuracy of LLM-generated analysis.

Experience with statistical forecasting or machine learning methods such as time-series forecasting and regression, using libraries like scikit-learn or statsmodels.

Clear communicator who can explain technical work to non-technical finance audiences and enable others through training, documentation, or office hours.