AI Data Specialist

Accordion•Boston, MA

3h•Hybrid

About The Position

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 1,400+ finance & technology experts in a supportive, collaborative environment. Backed by premier private equity firms and headquartered in New York with 10 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. 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. 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. 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.

Requirements

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
Position is not eligible for immigration sponsorship

Responsibilities

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
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