AI Data Specialist

Company Overview 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. Accordion Intelligence 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. Role 1: 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. What You’ll Do Build & Maintain Infrastructure: Construct data pipelines, models, and integrations that AI systems depend on, from raw source data to production-ready outputsCustom ML Modeling: Scope, design, and write custom ML models tailored to client problems, from feature engineering through evaluation and deploymentExploratory Data Analysis: Explore unfamiliar datasets rapidly to identify structure, surface anomalies, and form clear points of viewDiagnose Data Quality: Catch quality issues quickly, quantify their impact, and drive resolution proactivelyClient 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 stakeholdersNavigate Enterprise Data Environments: Work fluently within ERP systems, BI platforms, and financial data infrastructure Success in the First 6 Months Own end-to-end data delivery across multiple AI engagementsEstablish a reputation for finding data problems before they impact the teamRun direct client working sessions on data scope, quality, or access with confidenceDemonstrate faster, higher-quality output by weaving AI tools into your daily workflow What You’ll Bring Full-Stack Data Skills: Deep expertise in SQL, Python, machine learning, data modeling, and pipeline development with messy, real-world source systemsData Quality Instincts: Ability to find issues, quantify impact, and communicate findings proactivelyFast-Paced Delivery: Comfort operating in fast-moving, sprint-based consulting environments with shifting requirementsClient-Facing Communication: Ability to translate technical findings into plain language and build credibility quicklyFinance / PE Context: Familiarity with ERP systems, FP&A data, financial close processes, or portfolio company data infrastructureAI Tool Integration: Daily use of AI tools across exploration, modeling, documentation, and communication Role 2: 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. What You’ll Do Agentic Systems & RAG: Design and build multi-agent systems and RAG pipelines that automate financial workflows for PE-backed portfolio companiesFull-Stack Ownership: Own the pipeline from prompt engineering and tool design through deployment and monitoringEval-Driven Development: Define success metrics and evaluation methodologies *before* buildingArchitectural Defense: Present and defend architectural decisions to technical and non-technical audiences, including CFOs and PE operatorsObservability & Infrastructure: Build observability and evaluation infrastructure to continuously track and improve production AI system quality Success in the First 6 Months Ship at least one end-to-end agentic solution in a client engagement from scoping through productionEstablish evaluation infrastructure for your pod’s systems to measure quality and catch regressions earlyDemonstrate materially faster delivery cycles through daily use of AI toolsBuild a reputation with clients and teammates as a trusted owner of complex problems What You’ll Bring Experience: 3–8+ years of software engineering experience with a significant focus on AI/ML systems, agent development, or applied LLM engineeringProduction LLM Applications: Hands-on experience building RAG systems, agentic workflows, tool calling, and multi-agent orchestrationTech Stack: Proficiency in Python; experience with frameworks like LangChain, LangGraph, AutoGen, DSPy, or equivalentsEvaluation & 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 spaceDomain experience in finance or private equity (FP&A, GL data, NetSuite, SAP)Experience building multi-tenant architectures or tools for cross-client reusePrior consulting, professional services, or client-facing delivery experience Compensation, Location & Key Details Base Salary Range: $144,500 – $230,000 USD + significant bonus + benefitsLocation: Based in any US Accordion office location (Hybrid: flexibility to work remotely 2 days a week; must be local to office)Immigration: Position is not eligible for visa sponsorship