Top Artificial Intelligence Data & Analytics Jobs in Nashville, TN

Supports real-time media monitoring, stakeholder insight analysis, data preparation, alerts, briefings, and client reports. Responsibilities include tracking news across platforms, creating Excel pivot tables, updating charts and graphics, interpreting reputation-risk scoring, conducting research, and translating data into clear narratives. The intern collaborates with analysts to identify trends and emerging risks while developing analytical, writing, and business intelligence skills.

Own solution design, implementation, and debugging for Hightouch’s Identity Resolution product. Work directly with customer data architectures, configure identity models, write SQL against data warehouses, investigate data discrepancies, explain system behavior, and surface product gaps. Partner with sales engineering, support, technical account management, and executives to deliver customer solutions and advise on data strategy. Use coding agents while reviewing implementation quality and maintaining best practices.

Develop marketing budget allocation models, Early LTV metrics, performance diagnostic frameworks, and analytical tools. Analyze products, channels, campaigns, cohorts, attribution, and marketing economics using SQL, experimentation, BI tools, and spreadsheets. Support AI-based analytics development and unified BI metrics while translating findings into business rules and recommendations.

Own and evolve end-to-end data pipeline architecture across ingestion, transformation, modeling, and serving. Lead platform improvements for cost efficiency, reliability, access control, observability, orchestration, CI/CD, and developer experience. Drive complex cross-team initiatives, resolve production issues, influence architectural decisions, mentor engineers, and build automation incorporating AI into data engineering workflows.

Organize and curate large volumes of raw information: extract key points from transcripts, maintain clean spreadsheets, curate links/resources, tag and categorize content, and follow or propose systems for information structure. Part-time remote role with initial 10 hours/week and potential to grow.

Analyze healthcare and clinical operations data to deliver actionable customer insights, physician performance reporting, care-quality dashboards, and scalable BI solutions. The role requires SQL-based data transformation, dashboard development, collaboration with clinical, product, engineering, and client-facing teams, and clear communication of findings. The analyst will also use AI tools for query generation, data cleaning, pattern recognition, and analytical summaries.

Own the architecture, roadmap, and operation of Clarium’s AWS-based data platform. Build reliable batch and near-real-time pipelines, model healthcare supply chain data, optimize Postgres and Snowflake performance, and establish standards for testing, deployment, quality, and governance. Partner cross-functionally to create durable data models, lead multi-quarter initiatives, manage sensitive data controls, and mentor engineers.

Build and operate core data infrastructure for edge and hybrid environments, including ingestion, OT/IoT integrations, real-time streaming, data contracts, schemas, validation, lineage, observability, telemetry, and scalable query access. The role requires production ownership, reliability practices, customer integration, and collaboration across engineering and customer-facing teams. Candidates should have 6+ years of experience with production data, streaming, or integration systems and proficiency in Python and/or Go.

Design, develop, and operate large-scale data ingestion, transformation, enrichment, and processing pipelines across cloud, hybrid, and on-premises environments. Build distributed services using Java, Python, and Kafka; optimize data models and platform performance; deploy applications on Kubernetes; support infrastructure automation and CI/CD; troubleshoot production systems; and collaborate with customers and solution architects on implementations.

Build and maintain finance data pipelines, warehouse models, dashboards, and automations for unit economics, technology budgeting, burn, runway, margins, deal desk, and sales compensation. Develop internal tools and LLM-based agents that help non-engineers access trusted financial data. Partner with finance, sales, and engineering leadership to reconcile discrepancies and improve revenue, cost, billing, payroll, cloud-spend, and accounting workflows.