AI Data Specialist

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.

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

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

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

Qualifications

• Master's degree in Computer Science, Statistics, Data Science, Finance, or related field
• Minimum of 5 years of relevant work experience in data science, machine learning, or related fields
• Proven track record of delivering high-quality data solutions and driving business outcomes
• Experience with data engineering tools and techniques, including ETL, data warehousing, and data integration
• Experience with machine learning and statistical modeling, including feature selection, model training, and model evaluation
• Experience with data visualization tools and techniques, including Tableau, PowerBI, or similar
• Experience with cloud-based data processing platforms, such as AWS, Google Cloud, or Azure
• Experience with financial data and analysis, including FP&A, financial reporting, and financial modeling

Skills

• SQL, Python, machine learning, data modeling, pipeline development, and hands-on experience with messy, real-world source systems
• Strong instincts for data quality: finding the problem, quantifying the impact, and communicating 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: understanding 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: documenting a model, writing a findings summary, producing compelling stories from messy data, and drafting a client-facing issue log clearly and quickly

Benefits

• Comprehensive health insurance coverage
• Flexible vacation and sick leave policies
• Professional development opportunities and training programs
• Employee assistance program
• Generous retirement savings plan

Pay

The annual salary for this role ranges from: $144,500 to $230,000 USD + significant bonus + benefits. Actual compensation packages are determined by evaluating a wide array of factors unique to each candidate, including but not limited to geographic location, skill set, years and depth of experience, education, certifications, cost of labor and internal equity.

Schedule

This role is a hybrid role with the flexibility to work remotely 2 days a week. Ideal candidates should be local to the desired location.