Finance AI Analyst

Intuitive•Peachtree Corners, GA

1d•$93,200 - $148,400•Onsite

About The Position

The Finance AI Analyst will design and deploy AI/ML-driven analytics solutions within the Finance franchise of Enterprise Analytics. The role combines data engineering skills with practical experience applying AI/ML techniques to financial data, translating business requirements into intelligent, automated insights. The position is fully onsite and has a day shift. Compensation depends on job level, experience, skills, and qualifications. Target annual salary ranges are $109,700–$148,400 for Region 1 and $93,200–$126,100 for Region 2.

Requirements

At least 2 years of experience in data engineering, analytics engineering, or applied AI/ML roles.
Strong SQL proficiency and hands-on experience with Snowflake.
Experience building and deploying ML 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 GL, AP, AR, fixed assets, and cost accounting, or with SAP Finance.
Experience with BI 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

Hands-on experience with Snowpark, Cortex AI, or Snowflake ML functions.
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.
Experience with Tableau.

Responsibilities

Design, build, and maintain AI/ML models and pipelines for finance analytics use cases, including forecasting, anomaly detection, classification, and NLP on financial documents.
Develop and optimize data models in Snowflake, leveraging 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 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.