Senior AI Business Analyst - Finance & Data Strategy

Banyan Software•Toronto, ON

6h•CA$130,000 - CA$170,000•Hybrid

About The Position

The Senior Finance AI Business Analyst plays a critical role within Banyan's finance organization, partnering across Tax, Controllership, Treasury, FP&A, and Opco business partnering. You will operate inside the finance team, understanding and reimagining its workflows, and executing the high-value AI and data science initiatives that make finance faster, sharper, and smarter. You will join a team responsible for the financial backbone of more than 100 portfolio companies, sitting on a vast quantity of high-quality financial data waiting to be leveraged. Think of yourself as a translator and a builder, fluent in what modern AI and data science can do and trusted by finance leaders and technical teams alike to bridge finance mechanics and technical capability. Partnering with our central AI & Data Science team, you will bring real AI capability to finance's biggest opportunities, spanning close and reporting, forecasting, cash and treasury management, tax compliance, and opco financial partnering, and help build the data foundation that powers it. This role is scoped broadly on purpose: you will be the first dedicated AI business partner embedded across all of finance, with the opportunity to shape how the role grows, including building and leading a team as adoption scales across Tax, Controllership, Treasury, FP&A, and Opco business partnering. This is a hybrid role that blends three jobs, all applied inside our finance organization: AI strategist: identify, prioritize, and build business cases for AI and data science that drive finance KPIs across every discipline you support. Data science translator: bridge finance stakeholders, from Controllership to Treasury to FP&A, and technical AI and data resources. Scope models and tools, then interpret outputs in clear, decision-useful terms. Builder and operator: prototype and ship working AI tools directly, help each finance team adopt them in their workflows, and measure real impact. Lean on the central AI & Data Science team for engineering support as complexity scales.

Requirements

You love building. You have shipped real AI tools, not just read about them: LLMs, retrieval, or agentic workflows applied to business problems. You can prototype and put something useful in users' hands yourself.
You have the background. 6 to 10 years across finance, accounting, FP&A, treasury, tax, strategy consulting, or a data science partner role, with at least 3 years in AI, ML, or data science work.
A bachelor's in a quantitative, business, technical, or accounting field.
You are technically grounded. Working command of machine learning concepts, model evaluation, data quality, and the limits of AI. You know what is possible, what is hard, and what is risky.
You are fluent in data. Strong SQL, working Python or R for exploration, and command of Excel and modern BI tools such as Looker, Power BI, or Tableau.
You think in business value. Strong financial acumen across accounting, treasury, and planning fundamentals. You can build a credible case in a spreadsheet and explain it clearly in a one-pager.
You translate. You can explain a model to a CFO or controller and turn their question into a well-scoped piece of analysis. The finance teams you have partnered with want to work with you again.
You bring judgment. Curiosity, humility, and the spine to push back. You stay genuinely interested in the work of the people you support across every finance discipline.

Nice To Haves

Direct experience inside Tax, Controllership, Treasury, or FP&A, ideally in software, SaaS, or vertical market software environments.
Hands-on work with LLMs, retrieval systems, or agentic workflows applied to finance or accounting operations.
Prior experience as an embedded analyst or business partner, rather than a member of a fully centralized analytics team.
A track record of measuring AI or data science ROI in production, not just in pilots.
Experience partnering with decentralized business units or portfolio companies.
A master's, CPA, or CFA is a plus, not a requirement.

Responsibilities

Embed yourself as a trusted partner across finance, including Tax, Controllership, Treasury, FP&A, and Opco business partnering. Learn the data, systems, KPIs, and pain points of each discipline well enough that the teams lean on you as their AI partner.
Identify and prioritize AI and data science opportunities that move finance's metrics, sequenced by impact, effort, and risk across every discipline you support.
Build the ROI case for each opportunity you pursue. Define what success looks like, the sensitivities, and what would make the work fail.
Translate in both directions: turn finance problems, from close automation to cash forecasting to tax workpaper prep, into well-scoped requirements for our AI and data science engineers, and turn model and tool outputs back into decisions the finance team can act on.
Partner with finance leadership on roadmap and prioritization. Help them say yes to the right work and no to the rest.
Drive adoption. Train finance teams, design workflows around what you build, and measure usage and real impact after launch.
Set the patterns. Define and track KPIs for everything you ship, from model performance and adoption to time saved, decision quality, and dollar impact. Share what works across the AI & Data Science group.
Help shape how this role grows. As adoption scales, help define the roadmap for expanding AI support across Tax, Controllership, Treasury, FP&A, and Opco business partnering, including building and leading a team.
Automate tax provision workpapers, accelerate data gathering for compliance filings, and build tools to flag exposure across multi-entity, multi-jurisdiction structures.
Automate close and reconciliation tasks, build anomaly detection for journal entries, and accelerate management reporting cycles.
Build cash flow forecasting and scenario models, automate covenant tracking and liquidity monitoring, and surface working capital insights.
Build forecasting and budgeting models, develop self-serve analytics for finance and portfolio leadership, and automate variance analysis and board reporting.
Build tools that help portfolio company finance leaders benchmark performance, surface cost and margin insights, and accelerate reporting back to the center.