AI Data Specialist

Company Overview

We are the better way to work in finance. As private equity’s value creation partner, we sit at the heart of PE where sponsors and CFOs meet. Through financial consulting rooted in data, technology, and AI, we help clients drive value where we support the office of the CFO to drive end-to-end value creation.

If you crave challenging work and are looking to grow, come solve complex issues alongside 1,600+ finance & technology experts in a supportive, collaborative environment.

Backed by premier private equity firms and headquartered in New York with 11 offices around the globe, we are a high-growth, entrepreneurial firm looking for people who want to be part of building something great. Come make your mark.

Data & Analytics

Accordion's Data & Analytics (D&A) team offers cutting-edge, intelligent solutions to a global clientele, leveraging a blend of domain knowledge, sophisticated technology tools, and deep analytics capabilities to tackle complex business challenges.

We partner with Private Equity clients and their Portfolio Companies across diverse sectors, including Retail, CPG, Healthcare, Media & Entertainment, Technology, and Logistics.

This role can be based in any of our US office locations and is a hybrid role with the flexibility to work remotely 2 days a week. Ideal candidates should be local to the desired location.

This position is not eligible for immigration sponsorship.

Accordion Intelligence Lab

The AI Lab is composed of leading software and AI engineers, designing agentic-AI solutions ahead of the market. Our group builds and operationalizes the AI systems that power Accordion’s consulting capabilities, from agentic architectures and RAG pipelines to evaluation frameworks and production observability.

The Lab works closely with practice leaders to translate research findings into scalable, reliable tools that advance Accordion’s AI-driven value creation work for PE clients and their portfolio companies.

About the Role

We're looking for a Data Specialist to join one of Accordion's AI-augmented delivery pods. These pods are purpose-built teams that combine AI engineering, data science, and product management to transform how PE-backed companies run finance and operations.

In this role, you are the person who makes data work in practice. You move fluidly between messy source systems and production-ready pipelines, and you do it fast. You'll work directly alongside AI engineers and product managers to scope data requirements, diagnose quality issues, and build the data foundations that AI systems depend on. Client-facing moments come with the territory. You'll need to ask sharp questions, explain what you're seeing in plain language, and earn trust quickly.

This is not a role for someone who needs clean handoffs and stable requirements. The data is complicated, the timelines are short, and the problems change. If that sounds like the environment where you do your best work, this is the role.

What You’ll Do

• Build and maintain the data pipelines, models, and integrations that AI systems depend on, from raw source data to production-ready outputs
• Scope, design, and write custom ML models tailored to client problems, from feature engineering through evaluation and deployment
• Explore unfamiliar datasets with speed and rigor: identify structure, surface anomalies, and form a clear point of view on what the data can and can't support
• Diagnose data quality issues quickly, communicate their impact clearly, and drive resolution without waiting to be asked
• Work directly alongside AI engineers and product managers to translate ambiguous client problems into reliable, well-documented data products
• Run client working sessions on data (source system walkthroughs, model findings, quality assessments) and own the room when you do
• Tell the data story to non-technical audiences: in a chart, in a slide, in a meeting with a CFO who doesn't have time for jargon
• Navigate enterprise data environments fluently (ERP systems, BI platforms, financial data infrastructure) and get things done inside them
• Use AI tools to move faster across the full scope of your work: exploration, modeling, documentation, and client communication
• Travel to client site as needed

Success in the First 6 Months

• Own end-to-end data delivery on multiple AI engagements, from initial source system assessment through production-ready pipelines
• Establish a reputation within your pod for being the person who finds the data problem before it becomes the team's problem
• Run direct client working sessions on data (scope, quality, or access) and leave the client confident in your read of the situation
• Demonstrate faster, higher-quality output because of how you use AI tools, not just that you use them

What You’ll Bring

• Deep, practical data skills across the full stack: SQL, Python, machine learning, data modeling, pipeline development, and hands-on experience with messy, real-world source systems
• Strong instincts for data quality: You find the problem, quantify the impact, and communicate what it means before anyone has to ask
• Experience working in fast-moving, sprint-based environments where requirements shift and you still ship
• Comfort working directly with clients: Asking the right questions, translating technical findings into plain language, and building credibility quickly
• Background in finance or PE-adjacent environments: ERP systems, FP&A data, financial close processes, or portfolio company data infrastructure
• Fluent with AI and ML workflows: Enough to understand what the engineers need from the data layer, why it matters, and the ability to teach what you know
• AI tools woven into how you work daily, with demonstrably faster output to show for it
• Strong written communication: you can document a model, write a findings summary, produce compelling stories from messy data, and draft a client-facing issue log clearly and quickly

This is a high-velocity environment. Sprints are short, client expectations are high, and we don’t slow down between engagements. If you’ve spent time in large enterprise engineering organizations where velocity is constrained by process, this will feel different.