Accounting Solutions Consultant

About the role

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Production demonstrations illustrate how AI agents execute core accounting workflows for enterprise clients.

Production demonstrations showcase how AI agents execute core accounting workflows for enterprise clients. This role explains technical accounting, compliance, and integration details to prospects and stakeholders.

The position evaluates opportunities using structured methods and identifies the strongest fit.

Account Executive strategies for complex cycles and enterprise deals are shaped through close collaboration.

Post-sales transitions are handled with clear documentation and information transfer.

Current accounting practices, industry trends, and competitor moves are tracked to maintain relevance.

Requirements

At least 3 years of experience in accounting, corporate finance, FP&A, investment banking, or Private Equity is expected. Experience in accounting, corporate finance, FP&A, investment banking, or Private Equity is required for this position.

Complex problems are decomposed into clear, repeatable steps that non-technical stakeholders can follow. The role breaks down complex problems into clear, repeatable steps.

Technical accounting concepts are explained with precision to both executives and accounting teams. The role explains technical accounting concepts to both executives and accounting teams.

Assumptions are questioned and lessons are adapted to new solutions using first principles reasoning. The role questions assumptions and applies lessons to new solutions through first principles reasoning.

An in-person presence in the NYC Flatiron office is preferred. An in-person presence in the NYC Flatiron office is required.

Nice to have

Early startup experience with rapid growth and ambiguous environments appears in candidate background. Early startup experience is common in this background.

Hands-on experience with accounting software such as QuickBooks, Xero, NetSuite, Bill dot com, Ramp, or similar platforms appears in candidate history. Hands-on experience with accounting software platforms is common in this background.

Practical notes

This position operates from the NYC Flatiron office with in-person collaboration and full-time hours. The role involves high workload velocity and requires readiness for extensive travel and due diligence activities.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Production-grade, long-horizon agents are deployed at scale across accounting workflows. Applied machine learning platforms power workflows at large accounting firms. A startup mindset thrives in environments with ambiguity and rapid growth.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.