The Role

We are hiring a Senior Business Analyst, AI & Data Science to serve as an embedded business partner to one of our internal functional teams. You will be assigned to a single functional vertical at hire: Finance, M&A, Business Development, Human Resources, Legal, or Operations. You will spend your days inside that team, learning their workflows, surfacing high-value AI and data science opportunities, and shipping work that makes them faster, sharper, and more effective.

You will report into our central AI & Data Science team and work day-to-day alongside the leaders and operators of your assigned vertical. Think of yourself as a translator and a builder: fluent in the function's metrics and mechanics, fluent in what modern AI and data science can do, and trusted by both sides to bridge the two.

This is a hybrid role that blends three jobs, all applied inside your assigned function:

• AI strategist: identify, prioritize, and build the business case for AI and data science use cases that move the function's KPIs.
• Data science translator: bridge functional stakeholders and technical AI and data resources. Scope models and tools, then interpret outputs in clear, decision-useful terms.
• Operations partner: help the function operationalize AI and data tools in their workflows, drive adoption, and measure real impact.

Quick Facts

What You Will Do

• Embed inside your assigned function (Finance, M&A, Business Development, Human Resources, Legal, or Operations). Learn its data, systems, KPIs, and pain points well enough that the team treats you as one of their own.
• Identify and prioritize AI and data science opportunities that move the function's metrics. Sequence them by impact, effort, and risk.
• Build clear, ROI-driven business cases for each opportunity. Define success metrics, sensitivities, and what would make the work fail.
• Translate business problems into well-scoped requirements for our AI and data science engineers. Translate model and tool outputs back into decisions the function can act on.
• Partner with the functional leader on roadmap and prioritization. Help them say yes to the right work and no to the rest.
• Drive adoption of AI and data tools inside the function. Train end users, design new workflows, and measure usage and outcomes after launch.
• Define and track KPIs for every initiative you support, including model performance, adoption, time saved, decision quality, and dollar impact.
• Stay close to the AI and data science landscape, including LLMs, agentic systems, and modern analytics tooling. Bring a clear, well-reasoned point of view to the team.
• Share what works across the AI & Data Science group so other functional partners benefit from your patterns and pitfalls.

What This Looks Like By Function

Your day-to-day will depend on the function you join. A few illustrative examples:

• Finance: automate close and reporting tasks, build forecasting and scenario models, surface working capital and cost insights, build self-serve analytics for finance leadership, accelerate management reporting cycles.
• M&A: build sourcing and pipeline analytics, develop scoring models for inbound and outbound targets, structure and enrich the deal database, automate market mapping and competitive research, support deal teams with data-driven insights.
• Business Development: build lead scoring and prioritization models, analyze outbound campaign performance, automate research workflows, develop dashboards for pipeline health, surface signals that predict engagement and conversion.
• Human Resources: build workforce and attrition analytics, model hiring funnel performance and time-to-fill, benchmark compensation, surface engagement and sentiment insights, and automate repetitive HR operations.
• Legal: automate contract review and clause extraction with LLMs, surface risk flags across the contract portfolio, build matter and legal spend analytics, and speed up vendor and NDA workflows.
• Operations: analyze process throughput and cycle times, forecast capacity and demand, model vendor and supplier performance, automate ticket and queue triage, and build dashboards that keep operational KPIs visible.

Who You Are

• 6 to 10 years of experience as a business analyst, strategy consultant, FP&A or finance analyst, BD or M&A analyst, product manager, or data science partner. At least 3 of those years involved AI, ML, or data science work.
• A bachelor's degree in a quantitative, business, or technical field. A master's degree is a plus, not a requirement.
• Working knowledge of machine learning concepts, model evaluation, data quality, and the limits of AI. You do not need to train models. You do need to know what is possible, what is hard, and what is risky.
• Strong comfort with SQL and a working familiarity with Python or R for data exploration. Strong Excel and modern BI tools (Looker, Power BI, Tableau, or similar).
• Strong financial and business acumen. You can build a credible business case in a spreadsheet and explain it clearly in a one-pager.
• Excellent communication skills. You can explain a model to an executive and translate an executive's question into a well-scoped piece of analysis or model.
• Track record of being trusted by a non-technical functional team. People you have partnered with want to work with you again.
• Curiosity, judgment, and humility. You ask good questions, push back when needed, and stay genuinely interested in the work of the people you support.

Bonus Points

• Direct experience inside Finance, M&A, or Business Development. Bonus for software, SaaS, or vertical market software environments.
• Hands-on work with LLMs, retrieval systems, or agentic workflows applied to internal 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.

Why Banyan

• Real ownership of a function's AI agenda. You will not be a ticket-taker on a centralized data team.
• A long-term horizon. We do not optimize for the next quarter, so the work you ship can compound.
• A team that treats AI as a serious capability, not a buzzword. We have already built internal AI tooling we use every day.
• A culture of accountable autonomy. We trust you to do the work.
• Competitive compensation, benefits, and meaningful long-term upside.

How to Apply

Send your resume and a short note about a time you brought AI or data science into a functional team's workflow. Tell us the impact, what made it hard, and what you would do differently next time.

If you have a preference between Finance, M&A, Business Development, Human Resources, Legal, and Operations, let us know. We will assign the vertical based on fit and team need, and we want to hear how you think about it.

We look forward to hearing from you.