Origin Staffing - Thoughts on Recruitment

Recruiting an AI-Ready Finance Data Analytics Manager for a Global Alternative Asset Firm | A Search Case Study

Executive Summary

Origin Staffing partnered with a global alternative asset firm to hire an AI-ready Finance Data Analytics Manager for a newly created role at the intersection of finance, data, automation, and process modernization.

The client needed someone who could sit between finance and technology. The finance team had data, reporting needs, and manual processes that were no longer scaling. The technology team had systems knowledge, but not always the finance context needed to translate those needs into the right output.

This was not a pure finance search. It was not a pure data search either. It was a search for hybrid talent that could help the firm modernize finance workflows through analytics, automation, better reporting, and practical AI-adjacent thinking.

The search was difficult because the market rarely produces candidates with both sides of the profile. Many candidates were strong in finance but light on systems. Others were strong in data but lacked the accounting and finance fluency needed to support users inside a complex investment platform.

The client initially valued direct experience with niche finance platforms, but we helped frame why exact-platform experience was only one part of the profile. The bigger question was whether the candidate could understand the data, learn the systems, translate business needs, and build tools that finance teams would actually use.

The candidate ultimately stood out because he brought a CPA foundation, audit experience, applied data science training, automation experience, dashboarding expertise, and a track record of building analytics solutions for finance and audit users.

The placement showed a broader lesson for companies hiring AI-ready finance talent: the best candidates are not just tool users. They are translators who understand finance, data, process, controls, and business adoption.

The candidate accepted the offer and later became involved in broader process modernization and analytics initiatives across the organization.

Why This Search Mattered

Finance teams are under pressure to do more with the data they already have.

For many firms, the issue is not a lack of information. It is the way the information moves. Data sits in different systems. Reporting depends on manual updates. Users rely on spreadsheets because they understand them, even when those spreadsheets are no longer the best tool for the business.

That was the core issue in this search.

The client had a growing finance function inside a complex alternative asset platform. The team needed better ways to manage reporting, automate recurring work, support data governance, and create cleaner outputs for finance and investment stakeholders.

The Finance Data Analytics Manager role was created to help solve that problem.

The hire needed to understand finance well enough to know what mattered. They also needed enough technical depth to work with data, systems, dashboards, automation tools, and end users.

That combination is hard to find.

Many companies describe this need as “AI experience.” That phrase can be useful, but it is often too broad. In practice, the need is usually more specific.

Do you need someone to automate manual workflows?
Do you need better reporting?
Do you need cleaner data?
Do you need someone who can evaluate where AI fits safely into a finance process?
Do you need someone who can train finance users on a new tool?

Those are different problems. They require different strengths.

This search showed why defining the actual need matters before going to market.

The Real Meaning of AI-Ready Finance Talent

AI-ready finance talent is not just someone who has used an AI tool.

In finance and accounting, AI-ready talent means someone can understand a business process, identify where time is being lost, evaluate the quality of the data, and determine whether automation or analytics can improve the output.

That requires more than technical curiosity. It requires business judgment.

Recent Harvard Business School Working Knowledge research noted that demand is rising for roles that combine analytical, technical, and creative work that can be enhanced by artificial intelligence. It also emphasized that in finance, human judgment still matters when AI is used to process and evaluate data.

That point fits what we are seeing in the market.

Most finance teams are not asking for someone to “do AI” in the abstract. They are asking for someone who can make better use of technology in a controlled, practical way.

That includes:

• Reducing manual work
• Improving reporting accuracy
• Building dashboards users can trust
• Automating repeatable processes
• Translating business requirements into technical workflows
• Helping finance teams adopt new systems
• Knowing when an output needs human review

AICPA & CIMA has also framed AI for accounting professionals around practical workflow integration, financial analysis, business performance, and responsible use.

That is the talent market this search lived in.

The client did not need a pure engineer. They did not need a traditional finance manager. They needed a translator.

Why This Search Was Hard

The original profile included hands-on experience with niche finance platforms. That experience was valuable, but the candidate pool was extremely limited.

The client was open to related systems experience, but the bar remained high. The right person still had to understand finance, data, automation, reporting, and user adoption.

That created a narrow search.

We saw three common candidate profiles.

The first group had finance experience but limited data depth. These candidates understood the accounting and reporting environment, but they were not equipped to build scalable tools or rethink the process.

The second group had strong data skills but limited finance fluency. These candidates could speak about databases, dashboards, or workflows, but they struggled to understand what finance users actually needed.

The third group had some systems experience but lacked the communication skills needed to work across finance, technology, operations, and investment stakeholders.

The placed candidate stood out because he had a rare mix.

He had started in audit and accounting. He understood financial statements, controls, and the way finance teams think. He then moved into data analytics and automation work, where he helped build tools for audit and finance users. He also had experience training end users, managing analytics projects, and leading teams with mixed technical backgrounds.

That combination made him credible across both sides of the role.

Market Mapping – Where We Looked

Origin Staffing mapped the market across several candidate pools.

These included:

• Finance systems professionals
• Anaplan-adjacent and platform implementation candidates
• Finance transformation consultants
• Audit analytics professionals
• Power BI and Alteryx automation profiles
• Data science professionals with accounting exposure
• Finance managers with reporting automation experience

The search was not about finding one keyword. It was about finding the right blend.

Exact platform experience was helpful, but it was not enough on its own. A candidate could know a tool and still struggle to understand the finance use case. Another candidate could have strong finance experience but lack the technical ability to improve the process.

We focused on candidates who could explain projects clearly.

What was the business problem?
What data was needed?
How was the process working before?
What tool did they use?
What changed after implementation?
How did they train users?
How did they know the output was right?

Those questions mattered more than a list of software names.

Our Scorecard – Assessing AI-Ready Finance Talent

For this type of search, we did not evaluate talent through one lens.

The scorecard had to test several things at once.

Finance and accounting fluency
Could the candidate understand the business context behind the data?

Data literacy
Could the candidate work with structured data and explain how information moved between systems?

Automation mindset
Could the candidate identify where manual work could be reduced without losing accuracy or control?

Analytics and dashboarding experience
Could the candidate turn data into usable reporting?

Tool adaptability
Could the candidate learn a new platform even without direct experience in that exact system?

User empathy
Could the candidate build something finance users would actually adopt?

Business translation
Could the candidate explain technical work to non-technical stakeholders?

Controls and accuracy mindset
Could the candidate improve speed without sacrificing reliability?

Project ownership
Could the candidate move from problem definition to working output?

Curiosity and learning agility
Could the candidate operate in an evolving AI and automation landscape?

This is where our finance and accounting background mattered.

A general recruiter may hear “AI experience” and search for tools. We hear that phrase and ask what the company actually needs to fix.

Screening for Proof – Not Buzzwords

The strongest candidates could talk through real work.

We were not looking for someone who simply listed Python, SQL, Power BI, Alteryx, or AI tools on a resume. The question was whether the candidate could explain what those tools did for the business.

The screening focused on prompts like:

• Walk me through a reporting process you improved.
• What was manual before you got involved?
• What data did you need, and where did it come from?
• How did you know the output was accurate?
• How did you explain the tool to end users?
• What changed for the finance or audit team after implementation?
• Where could AI improve this process, and where would you still want human review?

This is where the placed candidate separated himself.

He could describe automation work through the lens of business impact. He could explain how tools improved audit efficiency, reporting quality, and user experience. He also understood that finance users do not adopt technology just because it is available. They adopt it when it solves a real problem and produces a reliable output.

That distinction matters.

Coaching the Candidate Through the Platform Gap

The candidate did not have direct experience with the client’s primary finance platforms.

That could have become the reason the process stalled.

We helped the candidate frame the gap correctly. The message was not, “I have used the exact same system.” The message was, “I understand how to work with data, learn the platform, identify the business requirement, and build toward the output.”

That was the right framing.

In finance systems and analytics roles, tools change. Platforms evolve. AI capabilities move quickly. The underlying skill is not memorizing one interface. The underlying skill is understanding how data flows, how users work, where controls matter, and how to turn a messy process into a cleaner one.

The candidate prepared heavily before each round. He researched the client’s platforms, studied likely use cases, and connected his prior automation experience to the problems this role was designed to solve.

That preparation gave the client confidence.

Interview Process – Testing Finance, Data, and Communication

The interview process tested the candidate from multiple angles.

The first round focused on fit, career story, finance experience, and the purpose of the role. The client wanted to know why the candidate was interested and whether his background connected to the firm’s needs.

There were also early questions around the investment environment. The client tested whether the candidate understood enough about finance, metrics, and business context to be credible with stakeholders.

The harder tests came from the data and operations side.

One interview pushed deeper into databases, technical structures, and data workflow. That conversation was challenging because the client was testing how technical the candidate really was. He was not a database engineer, and that was not the core value proposition. But he knew enough to hold the conversation and show that he could learn.

The second round went deeper on business acumen, strategy, product thinking, and technical adaptability. The candidate improved as the process moved forward. He connected his experience more clearly to the role and showed stronger command of how finance, data, and automation fit together.

That progression was important.

The client was not hiring a finished product for every platform. The client was hiring someone who could grow with the role and help shape the function.

Offer Design and Closing

The candidate had other finance opportunities in process.

What made this role different was that it did not ask him to choose between finance and automation. It combined both.

The opportunity offered:

• A prestigious platform
• Complex finance and investment exposure
• A newly created role
• Meaningful ownership
• Process modernization work
• Room to apply data, analytics, automation, and AI-adjacent thinking

For the candidate, that mattered. Other roles were more traditional finance seats. They did not fully use the automation and analytics skill set he wanted to keep building.

For the client, the decision came down to confidence. They needed to believe he could step into a complex environment, learn the systems, speak the language of finance users, and help build better processes.

The offer came together smoothly because both sides understood the fit.

The candidate joined in early 2025. Since then, feedback has been strong. He has applied data analytics and process automation to improve workflows and has been brought into broader initiatives where that hybrid skill set can create value across the organization.

Companies hiring for AI-ready finance talent should stop over-focusing on one exact tool or one exact industry background.

Those things can help. They should not be the whole screen.

The better screen is whether a candidate can explain real projects in detail. What did they build? What did they improve? What data did they use? What changed for the business? How did they protect accuracy? How did they train users?

In finance and accounting, AI talent is not just about tools. It is about understanding the business need and knowing how to bridge the gap between problem and output.

The best candidates sit between finance and data.

They understand the accounting or finance pain point. They also understand how technology can reduce manual work, improve reporting, and create better insight.

That is the talent companies need more of as AI continues to reshape finance workflows.

What We Did Differently

We did not treat this as a keyword search.

The client’s platform experience preference mattered, but we understood that exact tool experience was only one part of the profile. The deeper question was whether the candidate could translate finance needs into scalable analytics and automation work.

That required a more technical intake, deeper candidate screening, and a real understanding of how finance teams operate.

We evaluated candidates from the perspective of the hiring team:

If this were our finance process, who would we trust to fix it?
Who could understand the business need?
Who could speak to IT?
Who could build credibility with finance users?
Who could improve the process without creating new control risks?

That lens changed the search.

The strongest candidate was not simply the person with the closest platform match. It was the person who could connect finance, data, automation, and user adoption in a way that created practical value.

FAQ

What does AI-ready finance talent mean?
AI-ready finance talent means someone can use data, automation, analytics, and emerging tools to improve finance processes. The best candidates understand both the business problem and the technology needed to solve it.

Should companies require direct AI experience for finance roles?
Not always. Direct AI experience can help, but many companies are really looking for process improvement, automation, reporting, and data translation skills. The key is defining the actual business need first.

Why is exact platform experience often too narrow?
Exact platform experience can shrink the candidate pool quickly. Strong candidates can often learn new systems if they understand data flows, finance processes, controls, and user requirements.

What should hiring managers ask candidates with automation experience?
Hiring managers should ask candidates to explain specific projects. They should cover the business problem, the data used, the tools applied, the output created, and the impact on users.

Why do finance teams need translators between finance and technology?
Finance users know the business need, while technology teams often know the systems. The right hire can connect both sides and turn business requirements into usable tools.

What made this Finance Data Analytics Manager search difficult?
The search was difficult because the client needed finance fluency, data literacy, automation experience, user training skills, and learning agility. Few candidates brought all of those strengths together.

How does Origin Staffing assess AI-ready finance candidates?
We evaluate project depth, business translation, tool adaptability, process improvement experience, data fluency, and communication skills. The goal is to separate real builders from resume buzzwords.

What is the biggest mistake companies make when hiring for AI in finance?
The biggest mistake is asking for “AI experience” without defining the problem. Companies need to know whether they need automation, reporting, process design, governance, data cleanup, or user enablement.

Work with Origin Staffing

At Origin Staffing, we partner with companies that need finance and accounting talent capable of operating in more technical, data-driven environments. Our work starts with understanding the business problem, not just the job description.

This search was led by Brian Henry – Recruiting Manager, and Andrei Nikulin – Head of Recruiting at Origin Staffing. Contact us here to learn more about our service offerings.