AI Data Specialist

Accordion is the value creation partner for private equity, sitting at the intersection where sponsors and CFOs meet. Through financial consulting rooted in data, technology, and AI, Accordion supports the office of the CFO to drive end-to-end value creation alongside 1,600+ finance & technology experts across 11 global offices.

About the AI Lab

The AI Lab is composed of leading software and AI engineers designing agentic-AI solutions. The 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.

Data Specialist (AI-Augmented Delivery Pods)

About the Role

As a Data Specialist in an AI-augmented delivery pod (combining AI engineering, data science, and product management), you make data work in practice. You move fluidly between messy source systems and production-ready pipelines at high speed, working directly alongside AI engineers and product managers to scope data requirements, diagnose quality issues, and build the data foundations that AI systems depend on.

Responsibilities

• Build & Maintain Infrastructure: Construct data pipelines, models, and integrations that AI systems depend on, from raw source data to production-ready outputs
• Custom ML Modeling: Scope, design, and write custom ML models tailored to client problems, from feature engineering through evaluation and deployment
• Exploratory Data Analysis: Explore unfamiliar datasets rapidly to identify structure, surface anomalies, and form clear points of view
• Diagnose Data Quality: Catch quality issues quickly, quantify their impact, and drive resolution proactively
• Client Engagement & Storytelling: Run client working sessions (source system walkthroughs, model findings, quality assessments) and translate complex data findings into plain language for CFOs and non-technical stakeholders
• Navigate Enterprise Data Environments: Work fluently within ERP systems, BI platforms, and financial data infrastructure

Requirements

• Full-Stack Data Skills: Deep expertise in SQL, Python, machine learning, data modeling, and pipeline development with messy, real-world source systems
• Data Quality Instincts: Ability to find issues, quantify impact, and communicate findings proactively
• Fast-Paced Delivery: Comfort operating in fast-moving, sprint-based consulting environments with shifting requirements
• Client-Facing Communication: Ability to translate technical findings into plain language and build credibility quickly
• Finance / PE Context: Familiarity with ERP systems, FP&A data, financial close processes, or portfolio company data infrastructure
• AI Tool Integration: Daily use of AI tools across exploration, modeling, documentation, and communication

Success in the First 6 Months

• Own end-to-end data delivery across multiple AI engagements
• Establish a reputation for finding data problems before they impact the team
• Run direct client working sessions on data scope, quality, or access with confidence
• Demonstrate faster, higher-quality output by weaving AI tools into your daily workflow

AI Engineer (AI-Augmented Delivery Pods)

About the Role

As an AI Engineer in an AI-augmented delivery pod, you design and build the AI systems that power client engagements: agentic workflows, RAG pipelines, evaluation frameworks, and production-grade tools operating in real PE environments. You will work directly with clients and cross-functional pod teammates from day one at sprint pace.

Responsibilities

• Agentic Systems & RAG: Design and build multi-agent systems and RAG pipelines that automate financial workflows for PE-backed portfolio companies
• Full-Stack Ownership: Own the pipeline from prompt engineering and tool design through deployment and monitoring
• Eval-Driven Development: Define success metrics and evaluation methodologies before building
• Architectural Defense: Present and defend architectural decisions to technical and non-technical audiences, including CFOs and PE operators
• Observability & Infrastructure: Build observability and evaluation infrastructure to continuously track and improve production AI system quality

Requirements

• Experience: 3–8+ years of software engineering experience with a significant focus on AI/ML systems, agent development, or applied LLM engineering
• Production LLM Applications: Hands-on experience building RAG systems, agentic workflows, tool calling, and multi-agent orchestration
• Tech Stack: Proficiency in Python; experience with frameworks like LangChain, LangGraph, AutoGen, DSPy, or equivalents
• Evaluation & Data Engineering: Strong grasp of eval methodologies (catching regressions, output quality scoring) and data engineering fundamentals (ETL, SQL/NoSQL, vector databases, pipelines)
• AI Tool Mastery: AI tools woven into your daily workflow with concrete, demonstrable speed improvements

Nice to Have

• Open-source contributions in the AI/ML space
• Domain experience in finance or private equity (FP&A, GL data, NetSuite, SAP)
• Experience building multi-tenant architectures or tools for cross-client reuse
• Prior consulting, professional services, or client-facing delivery experience

Success in the First 6 Months

• Ship at least one end-to-end agentic solution in a client engagement from scoping through production
• Establish evaluation infrastructure for your pod’s systems to measure quality and catch regressions early
• Demonstrate materially faster delivery cycles through daily use of AI tools
• Build a reputation with clients and teammates as a trusted owner of complex problems

Pay

Base Salary Range: $144,500 – $230,000 USD + significant bonus + benefits

Schedule

Hybrid: flexibility to work remotely 2 days a week; must be local to office

Location

Based in any US Accordion office location