AI Data Specialist

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.

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. Sprints are short, client expectations are high, and we don’t slow down between engagements. If you’ve spent time in large enterprise engineering organizations where velocity is constrained by process, this will feel different.

The people who thrive here are those who find the pace energizing, not exhausting. They ship fast because they’ve internalized good judgment (not because they cut corners). They use AI tools to multiply their output, communicate clearly with non-technical stakeholders, and course-correct quickly when something isn’t working.

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.

Accordion is an Equal Opportunity Employer. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical​​​ condition, pregnancy, genetic information, gender, sexual orientation, gender identity or ​expression, veteran status, or any other status protected under federal, state, or local law.

Please note that as per Accordion policy, we do not accept unsolicited resumes from third-party recruiters unless such recruiters are engaged to provide candidates for a specified opening and in alignment with our inclusive diversity values. Any employment agency, person or entity that submits an unsolicited resume does so with the understanding that Accordion will have the right to hire that applicant at its discretion without any fee owed to the submitting employment agency, person or entity.

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