Senior Data & AI Analyst, Finance Systems

AeroVironment•Simi Valley, CA

3d•Onsite

About The Position

The Senior Data & AI Analyst, Finance Systems owns financial data migration for Oracle Fusion implementation while pioneering the use of AI, large language models (LLMs), and intelligent agents to transform finance operations. This role combines traditional data conversion excellence with cutting-edge AI capabilities, building intelligent workflows for data quality, process automation, anomaly detection, and predictive analytics. This position reports directly to the Director, Finance Systems and serves as the technical lead for data and emerging AI initiatives.

Requirements

Bachelor's degree in Finance, Accounting, Computer Science, Data Science, or related field
5+ years of experience in financial data management, ERP data migration, or business intelligence/analytics
2+ years hands-on experience building AI/LLM applications, intelligent agents, or machine learning models (personal projects, hackathons, or professional work)
Proven experience leading at least 2 full-cycle ERP data conversions from planning through post-go-live stabilization
Expert-level proficiency with Python (pandas, numpy, scikit-learn) for data transformation, analysis, and machine learning
Hands-on experience with LLM APIs (OpenAI GPT, Anthropic Claude, Google Gemini), prompt engineering techniques, and AI agent frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) including retrieval-augmented generation (RAG) architectures
Strong SQL skills for data extraction, transformation, and validation across relational databases
Experience with data integration tools (Oracle FBDI, APIs, SSIS, Informatica, Alteryx), cloud data platforms (Oracle Cloud, AWS, Azure), and software development best practices including version control (Git) and CI/CD pipelines for production AI deployments
Experience with Oracle EPM Cloud data structures, integration patterns, and reporting (PBCS, FCCS, Planning preferred)
Strong troubleshooting skills for Oracle Fusion reporting tools (OTBI, BIP, Smart View) and third-party BI platforms (SplashBI, Power BI, Tableau)
Working knowledge of data governance principles, MDM concepts, and data quality frameworks
Experience supporting Apex application rollouts through data integration and connectivity
Strong understanding of financial accounting principles, chart of accounts structures, and subledger-to-general ledger reconciliation
Experience with Oracle Fusion data models (Procure to Pay, Record to Report, Quote to Cash, Project to Close) or comparable ERP data structures
Exceptional analytical skills with ability to investigate complex data issues, identify root causes, and implement sustainable solutions
Meticulous attention to detail and commitment to accuracy—zero tolerance for data quality compromises
Strong documentation skills with ability to create clear, comprehensive technical documentation for both technical and non-technical audiences
Entrepreneurial mindset with passion for exploring emerging technologies and building innovative solutions
Ability to translate business problems into AI/data science solutions and articulate value propositions to non-technical stakeholders
Comfortable working in ambiguity, experimenting with new approaches, and iterating based on feedback
Collaborative approach with strong communication skills for presenting technical concepts, demonstrating prototypes, and building stakeholder buy-in across Finance, IT, and external AI/data science communities
Ability to work in an office environment (Constant)
Required to sit and stand for long periods; talk, hear, and use hands and fingers to operate a computer and telephone keyboard (Frequent)
U.S. person under the ITAR and the EAR, or a person to be approved for an export license by the governing agency
Current U.S. Citizenship may be required due to contract requirements

Nice To Haves

Master's in Data Science, AI, or related field preferred

Responsibilities

Lead end-to-end data conversion planning for Oracle Fusion, including scope definition, data source identification, mapping specifications, and conversion schedules
Extract, transform, and load financial data from legacy systems (Chart of Accounts, customer/vendor masters, open balances, historical transactions, asset registers)
Develop and maintain data mapping documents that translate legacy data structures to Oracle Fusion Cloud data models (Procure to Pay, Record to Report, Quote to Cash, Project to Close)
Execute iterative mock conversion cycles (Mock 1, Mock 2, Final) with progressively tighter quality gates and validation checkpoints
Coordinate with IT Infrastructure team on technical data extraction, FBDI file generation, and API-based data loading
Design and implement comprehensive data quality rules, reconciliation controls, and exception handling processes; build automated validation scripts, profiling reports, and dashboards using Python, SQL, and AI/ML frameworks
Partner with Accounting SMEs to validate converted balances, ensure trial balance integrity, and resolve discrepancies
Develop predictive models for financial forecasting, anomaly detection in journal entries, and intelligent close process monitoring
Design and build AI-powered intelligent agents using large language models (GPT, Claude, Gemini, or open-source alternatives) for finance process automation
Develop LLM-based data quality agents that automatically detect anomalies, validate business rules, and suggest corrections during data migration and ongoing operations
Create conversational AI interfaces for finance users to query data, generate reports, and troubleshoot issues through natural language
Build intelligent document processing pipelines using LLMs for invoice extraction, contract analysis, and regulatory filing automation
Implement prompt engineering frameworks, retrieval-augmented generation (RAG), fine-tuning strategies, and governance frameworks for responsible AI use including bias detection, explainability, and audit trails
Support data migration and integration for Oracle EPM Cloud (PBCS, FCCS, Planning) connecting to Oracle Fusion actuals and subledger data
Troubleshoot reporting issues in Oracle Fusion (OTBI, BIP, Smart View, FBDI) and SplashBI, diagnosing data quality problems and performance bottlenecks
Partner with FP&A and Accounting on EPM data flows, dimension hierarchies, and planning/forecasting data structures
Support Apex application rollout through data integration, ensuring clean data pipelines between Apex, Fusion, and EPM environments
Establish data governance frameworks for master data (COA, customers, vendors, projects) including ownership, quality rules, stewardship processes, data lineage documentation, and metadata repositories for regulatory compliance
Support finance transformation projects through data analysis, migration support, and capitalization documentation
Coordinate with Business Units, IT Infrastructure, and external vendors to secure data extracts, coordinate conversion windows, and troubleshoot technical issues
Partner with Senior Manager, Finance Systems to align data conversion schedules with functional configuration timelines and testing cycles
Evangelize AI/LLM capabilities across Finance organization through proof-of-concepts, workshops, lunch-and-learns, and documentation; build internal capability by training team members on effective LLM usage
Maintain knowledge of emerging AI technologies, attend conferences/training, and share best practices through cross-team collaboration
Provide ongoing production support for existing finance systems data flows, reporting, and integrations (Oracle Fusion, EPM, Apex, and other applications); execute data migration activities for Oracle Fusion and other finance system implementations; own hypercare support for data-related issues (balance mismatches, missing records, data correction requests)
Develop and maintain ongoing data quality monitoring processes leveraging AI agents for real-time anomaly detection; document data lineage, transformation logic, business rules, and AI model documentation for audit compliance
Train Finance Systems team on data maintenance procedures, AI agent usage, and troubleshooting techniques