Job Title

About the RoleWe are modernizing how our Finance team plans, forecasts, and reports. As a manufacturing and distribution business, our financial outcomes are tied closely to inventory, COGS, distribution costs, supplier terms, and customer demand — and we want our FP&A team to use modern data tools and AI to see those signals earlier and act on them faster.This is a newly created role to bring data science, machine learning, and AI into FP&A. You will help our analysts move from manual, spreadsheet-heavy work to governed, automated, AI-assisted workflows — and you will personally build the forecasts, dashboards, and AI assistants that change how Finance partners with the business. You will work with tools like Snowflake, Planful, Python, Power BI, and modern LLMs, and your job is to make them practical and useful for the rest of the FP&A team.No Finance Degree is required, but you should understand business drivers, controls, and how to turn technical capabilities into practical FP&A solutions.Key ResponsibilitiesBuild the FP&A data foundation — bring together GL (SAP Business One), planning (Planful), sales and customer data (Salesforce), T&E (Expensify), and procurement spend (Procurify) into a single, trusted finance data set in Snowflake. Make sure it is reliable, well-documented, and ready for analysis.Modernize forecasting and scenario planning — replace static, manual budgets with rolling, driver-based forecasts for revenue, COGS, opex, headcount, inventory, and cash. Build scenario models so leaders can quickly see the impact of demand shifts, pricing, supplier costs, and FX. Feed outputs back into Planful so the planning cycle stays simple.Bring AI assistants into FP&A workflows — use LLMs and AI agents to draft variance commentary, summarize results, answer ad-hoc finance questions, and automate repetitive reporting tasks — always with a human review step.Catch anomalies before they become problems — build models that flag unusual GL postings, outlier expenses, and forecast drift so issues surface during the period, not after close.Partner across the business — translate Finance priorities into practical data and AI solutions; coordinate with IT, Data, and Security on tools, access, and architecture; explain risks and tradeoffs clearly to non-technical stakeholders.Help the FP&A team grow — run hands-on workshops on SQL, Python, no-code/low-code tools, prompt engineering, and AI agents so the rest of the team builds confidence using these tools day-to-day.Make complex tools easy to use — keep data warehousing, ML, and AI agents accessible to colleagues with varied technical backgrounds. The goal is to demystify, not to impress.Keep it governed and audit-ready — promote clear standards for AI use, certified data sources, model explainability, audit trails, and access controls; partner with Internal Audit and IT Security to keep our analytical work SOX-aligned.Required QualificationsBachelor's or Master's in Finance, Economics, Accounting, Data Science, Statistics, Computer Science, Engineering, Mathematics, or a related quantitative field.3 to 6 years of hands-on Python and SQL experience used in FP&A, finance analytics, or technical consulting work.Practical experience building forecasting, classification, or anomaly-detection models and putting them into use.Comfortable working with cloud data warehouses (Snowflake or similar) and modern data pipelines — basic data modeling, ETL/ELT, and performance tuning.Hands-on experience using LLMs / GenAI inside analytical workflows.Strong fluency in Power BI for dashboards and reporting.Working knowledge of finance controls, SOX, and why audit trails matter in analytical and AI-enabled environments.Preferred QualificationsHands-on familiarity with Snowflake (Snowpark, Cortex), Planful, Salesforce, SAP Business One, Expensify, or Procurify — or similar EPM, ERP, T&E, and procurement systems.Experience with no-code / low-code tools (e.g., Dataiku, Alteryx, Power Automate) that help non-technical users get started.Hands-on with AI agents, prompt engineering, retrieval-augmented generation (RAG), or fine-tuning LLMs for finance use cases.Understanding of data governance, data privacy, and security — especially in the context of LLM usage.Prior experience in a manufacturing or distribution business — working with inventory, COGS, supplier, and customer data — is a strong plus.