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. 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.