AI Data Analyst

Remitly•Dayton, OH

1d•$78,800 - $131,300

About The Position

Are you passionate about improving data quality and readiness to unlock the full potential of AI solutions? Do you enjoy collaborating across teams to ensure data is structured, governed, and usable for intelligent systems? About the Business: LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti-Money Laundering/Counter Terrorist Financing, Identity Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management. You can learn more about LexisNexis Risk at the link below, https://risk.lexisnexis.com About the Team: We are a newly formed Enterprise AI team focused on enabling agent-based solutions across the organization. We build and manage the environments, platforms, and guardrails that allow teams to create, test, and scale AI agents safely and efficiently turning experimentation into real business impact. We’re a team of curious builders and operators who are constantly exploring, learning, and applying new AI tools and approaches to solve real-world problems and improve how work gets done. About the Role: We are seeking an AI Data Analyst to support teams in preparing and maintaining AI‑ready data for use in AI tools, copilots, and intelligent agents. This role focuses on data readiness, quality, metadata, and governance, helping teams understand how to structure, document, and manage their data so it can be safely and effectively used by AI systems. The AI Data Analyst partners with data engineering, AI, and governance teams to assess data readiness, identify gaps and recommend improvements. This role does not own end‑to‑end data pipelines and is not expected to be a deep technical expert in RAG or embeddings, but should have a solid working understanding of AI‑driven data needs.

Requirements

Proven experience in data analysis, analytics engineering, data operations, or data quality roles.
Good understanding of data quality principles and how poor data impacts downstream systems.
Experience working with structured and unstructured data (tables, files, documents, knowledge assets).
Proficiency in SQL and comfort investigating data issues.
Familiarity with data governance fundamentals (classification, access controls, ownership, retention).
Strong communication skills and ability to explain data concepts to non‑technical stakeholders.

Nice To Haves

Exposure to AI‑enabled products, copilots, or search‑based solutions.
Basic familiarity with AI data concepts such as semantic search, embeddings, or retrieval patterns.
Experience working in enterprise or regulated environments.
Experience contributing to standards, playbooks, or shared data practices.

Responsibilities

AI Data Readiness Support: Work with product and delivery teams to assess whether datasets and content are fit for AI use cases. Help teams understand and apply AI data readiness standards, including quality, freshness, metadata, and access expectations. Identify common data issues that impact AI outcomes (e.g., stale data, unclear ownership, missing metadata) and recommend remediation steps. Contribute to repeatable checklists, guidance, or documentation that help teams prepare data for AI.
Data Quality & Relevance Support: Support data quality checks focused on accuracy, completeness, consistency, and timeliness for AI‑consumed data. Assist in monitoring and validating data freshness and relevance, escalating issues to engineering or data owners as needed. Help teams improve data clarity and usability to reduce ambiguity in AI outputs.
Metadata & Semantic Enablement: Assist teams in improving metadata, documentation, and business descriptions so AI systems can better interpret content. Support basic semantic labeling or categorization efforts that improve AI retrieval and reasoning (in coordination with engineering teams). Promote good content hygiene practices (clear structure, consistent naming, well‑scoped documents).
AI Data Sources & Retrieval (Support Role): Support the upkeep and documentation of approved data sources used by AI solutions. Help ensure data included in AI retrieval scenarios is appropriate, governed, and up to date. Collaborate with AI and platform teams on data inclusion/exclusion decisions without owning technical implementation.
Governance, Lineage & Compliance Awareness: Help teams align AI‑consumed data with enterprise governance requirements, including classification, access controls, and retention. Support basic data lineage and ownership documentation for AI‑relevant datasets. Partner with governance and security teams by surfacing risks or gaps; does not act as final approval authority.