Finance AI Analyst

Job description

Primary Function of Position The Finance AI Analyt will design and deploy AI/ML-driven analytics solutions within the Finance franchise of Enterprise Analytics. This role combines deep data engineering skills with hands-on experience in AI/ML techniques applied to financial data, translating business requirements into intelligent, automated insights. Roles and Responsibilities Design, build, and maintain AI/ML models and pipelines that support finance analytics use cases (forecasting, anomaly detection, classification, NLP on financial documents).

Develop and optimize data models in Snowflake, leveraging Cortex or equivalent tool AI functions. Debug and troubleshoot complex data models across the Enterprise Data Warehouse (EDW) stack — trace data lineage, validate transformations, and resolve 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 (LLMs, generative AI, vector search) into the analytics ecosystem.

Establish best practices for model monitoring, versioning, and governance.

Qualifications

Required Skills and Experience

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, ML functions preferred). Experience building and deploying ML models (Python, or similar). Ability to debug end-to-end data flows in a Data Warehouse environment — identify breakpoints in staging, transformation, and presentation layers. Familiarity with finance data domains (GL, AP, AR, fixed assets, cost accounting) or SAP Finance.

Experience with BI tools (Tableau preferred). Strong communication skills; ability to present AI concepts to non-technical finance stakeholders.

Preferred Qualifications

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. Required Education and Training Bachelor's degree in finance, computer science, mathematics, information systems, or business.