The Role

The Forward Deployed AI Engineer - Mergers & Acquisitions plays a critical leadership role within the Banyan M&A investment team, one of the most active software acquirors in the world. You will understand how the deals get done, reimagine the workflows behind them, and build the high-value AI and data science tools that make M&A run faster, sharper and smarter.

You will join a world-class operation sitting on a vast quantity of high-quality data waiting to be leveraged. Think of yourself as a builder and a translator, fluent in what modern AI and data science can do and trusted to bridge deal mechanics and technical capability. Given M&A functions in between both new business development and operations, you sit at the intersection of all three groups, contributing to the architecture of a true end-to-end system that becomes more valuable with each new opportunity and operating company data set.

This is a hybrid role that blends three jobs, all applied inside our investment team:

• Builder and operator: prototype and ship working AI tools directly, help the M&A team adopt them in their workflows, and measure real impact. Draw on the central AI & Data Science team for engineering support as complexity scales.
• AI strategist: identify, prioritize, and build business cases for AI and data science that drive M&A KPIs.
• Translator: bridge M&A stakeholders and technical AI/data resources. Scope models and tools, then interpret outputs in clear, decision-useful terms

Quick Facts

What You Will Do

• Build novel tools on top of one of the richest acquisition datasets anywhere. Tools that price a deal, speed up diligence and surface targets before competitors see them.
• Embed yourself as a core member of our investment team and own the process. Learn the data, systems, KPIs, and pain points well enough that the team leans on you as its investment systems architect.
• Help architect the data foundation for an AI-native M&A process. Connect the data that today lives in separate systems across the full transaction lifecycle, from screening through diligence to closing, with hooks into sourcing upstream and integration downstream.
• Find where AI and data science can most change how transactions get done. Then sequence the work by impact, effort, and risk.
• Make the ROI case for each one. Define what success looks like, the sensitivities, and what would make the work fail.
• Translate in both directions. Turn business problems into well-scoped requirements for our AI and data science engineers and turn model and tool outputs back into decisions the team can act on.
• Drive adoption. Train the team, design the 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 so we can reuse it in the next embedded role.

Who You Are

• You love building. You have shipped real AI tools: LLM applications, 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 software engineering, analytics, strategy, finance, or product, with at least 3 years hands-on in AI, ML or data science. A bachelor's in a quantitative, business, or technical field; a master's is a plus, not a requirement.
• 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 Python and SQL; you can prototype from raw data to a working tool. Command of Excel and modern BI tools such as Looker, Power BI, or Tableau.
• You think in business value. Strong financial and commercial acumen. 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 an executive and turn their question into a well-scoped piece of analysis. The functional 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.

Bonus Points

• Forward-deployed experience. Time in a forward deployed engineer, solutions engineer or hands-on consulting role where you built with the customer in the room.
• Deal-side experience. Direct time inside M&A or IB/PE, ideally in software, SaaS, or vertical market software.
• Embedded experience. You have worked as an embedded analyst or business partner inside an operating team.
• Production ROI. A track record of measuring AI or data science impact in production.

Why Banyan

• You own the agenda. You set M&A’s AI roadmap and decide what gets built.
• Built to hold forever. The tools you build are made to last and compound for years, deal after deal. We invest and operate on a permanent time horizon, free from the short-term pressure to satisfy investors each quarter.
• A front-row seat with people who do this at the highest level. You sit inside one of the most active serial acquirers in software and learn the business from the investors and operators doing the deals.
• A real AI foundation already in place. You step into a working AI-native M&A engine and take it further, building alongside a high-adoption, fast-moving team that already works this way from day one.
• Autonomy with a high bar. We trust you to make the call, and we expect you to own the outcome.
• Paid for the scope. Competitive base, performance bonus, full 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.

We look forward to hearing from you.

This position is for an existing vacancy.