Finance AI Analyst

Intuitive•Durham, NC

About The Position

The Finance AI Analyst designs and deploys AI- and machine-learning-driven analytics solutions within the Finance franchise of Enterprise Analytics. The role combines data engineering with applied AI/ML expertise to translate finance business requirements into intelligent, automated insights. The analyst builds and maintains models and pipelines for finance analytics, develops data models in Snowflake, troubleshoots data flows across the Enterprise Data Warehouse, and works with Finance stakeholders to bring production-grade solutions into use. The role also evaluates emerging AI technologies and establishes practices for model monitoring, versioning, and governance.

Requirements

At least 2 years of experience in data engineering, analytics engineering, or applied AI/ML roles.
Strong SQL proficiency.
Hands-on experience with Snowflake; experience with Snowpark, Cortex AI, or ML functions is preferred.
Experience building and deploying machine-learning models using Python or a similar language.
Ability to debug end-to-end data flows in a data warehouse environment and identify breakpoints in staging, transformation, and presentation layers.
Familiarity with finance data domains such as general ledger, accounts payable, accounts receivable, fixed assets, or cost accounting, or with SAP Finance.
Experience with business intelligence tools; Tableau is preferred.
Strong communication skills and ability to present AI concepts to non-technical Finance stakeholders.
Bachelor’s degree in finance, computer science, mathematics, information systems, or business.

Nice To Haves

Experience with LLM-based applications, RAG architectures, or Snowflake Cortex Agents.
Knowledge of finance portfolio management, FP&A, or treasury analytics.
Familiarity with SDLC and CI/CD for data/ML pipelines.
Hands-on experience with Snowflake Snowpark, Cortex AI, or ML functions.
Experience with Tableau.

Responsibilities

Design, build, and maintain AI/ML models and pipelines for finance analytics use cases, including forecasting, anomaly detection, classification, and natural-language processing on financial documents.
Develop and optimize data models in Snowflake, using Cortex or equivalent AI functions.
Debug and troubleshoot data models across the Enterprise Data Warehouse stack by tracing data lineage, validating transformations, and resolving discrepancies at the model layer.
Partner with Finance stakeholders to identify high-impact AI opportunities and translate them into production-grade solutions.
Evaluate and integrate emerging AI tools, including LLMs, generative AI, and vector search, into the analytics ecosystem.
Establish best practices for model monitoring, versioning, and governance.