Finance AI Analyst

Intuitive•Sunnyvale, GA

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 experience to translate business requirements into intelligent, automated financial insights. The analyst develops and maintains models and data pipelines, troubleshoots data models throughout the Enterprise Data Warehouse, works with Finance stakeholders to identify opportunities, integrates emerging AI tools, 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; Snowpark, Cortex AI, or ML functions are 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 BI tools.
Strong communication skills and ability to present AI concepts to nontechnical Finance stakeholders.
Bachelor’s degree in finance, computer science, mathematics, information systems, or business.

Nice To Haves

Experience with Tableau.
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.

Responsibilities

Design, build, and maintain AI/ML models and pipelines for finance analytics, including forecasting, anomaly detection, classification, and NLP on financial documents.
Develop and optimize data models in Snowflake, using Cortex or equivalent AI functions.
Debug and troubleshoot complex data models across the Enterprise Data Warehouse stack by tracing data lineage, validating transformations, and resolving model-layer discrepancies.
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.