Finance Analyst-Techno-Functional Financial Analyst – Finance, AI & Advanced Analytics - 6+yrs exp

Meet the Team Join a forward-looking Finance organization that combines financial analysis, business intelligence, automation, and emerging AI technologies to solve real business problems. The team partners closely with Finance, business stakeholders, Data Science, Engineering, Information Security, and Governance teams to develop secure, scalable, and practical analytical solutions. Your Impact As a Techno-Functional Financial Analyst , you will combine strong Finance and FP&A expertise with hands-on capabilities in Power BI, SQL, Python, Machine Learning, Generative AI, and Agentic AI . You will translate complex finance requirements into dashboards, predictive models, AI-powered applications, and automated workflows that improve reporting, forecasting, productivity, controls, and decision-making. This role requires practical experience in implementing AI/ML solutions for real business use—not just training, coursework, or proof-of-concept exercises. You Will Finance, Reporting & Business Analytics • Prepare and deliver periodic financial and management reporting covering bookings, revenue, gross margin, operating expenses, forecasts, and other key business metrics. • Partner with Finance Controllers, business leaders, and cross-functional stakeholders to understand requirements and provide accurate, timely, and actionable financial insights. • Perform variance analysis across actuals, forecast, budget, prior quarter, and prior year to identify drivers, trends, risks, and opportunities. • Support monthly and quarterly close activities through data validation, reconciliations, P&L reviews, and investigation of reporting discrepancies . • Develop and maintain interactive dashboards and executive reports using Power BI, Excel, and other BI tools . • Extract, integrate, and analyze large datasets using SQL, Python, Excel Power Query , and appropriate data-processing technologies. • Build forecasting, trend-analysis, and scenario-planning models to support management decisions. • Translate functional requirements into reporting solutions, calculation logic, data models, dashboards, and analytical products. • Perform data-quality checks and partner with Finance, Data Engineering, and Technology teams to resolve data and reporting issues. • Automate recurring reports, reconciliations, and manual finance activities using Excel, VBA, Power Query, Python, Power BI , or workflow-automation technologies. • Prepare executive summaries and presentations that clearly communicate business performance, financial risks, opportunities, and recommended actions . • Document data sources, business rules, calculations, reporting methodologies, procedures, and key controls. • Support user-acceptance testing, regression testing, implementation, and adoption of new finance systems, dashboards, and analytical solutions. • Identify opportunities to simplify, standardize, and improve finance reporting and planning processes . Generative AI, Machine Learning & Agentic AI • Identify finance use cases where Generative AI, Machine Learning, and Agentic AI can improve forecasting, reporting, productivity, controls, and decision-making. • Design, build, test, and implement AI/ML solutions for real finance and business requirements . • Develop predictive models for forecasting, classification, anomaly detection, risk identification , and other financial applications. • Perform data preparation, feature engineering, model selection, training, validation, and performance evaluation using appropriate ML techniques. • Apply techniques such as regression, time-series forecasting, decision trees, Random Forest, XGBoost , or equivalent methods based on the business problem. • Build Generative AI applications using Large Language Models (LLMs) for financial commentary, document analysis, summarization, question answering, and insight generation. • Develop Retrieval-Augmented Generation (RAG) solutions using approved internal documents or structured data to produce grounded and traceable responses. • Design Agentic AI workflows that retrieve data, perform analysis, apply business rules, and generate controlled outputs with human oversight. • Integrate AI solutions with approved data sources, APIs, databases, dashboards, and workflow tools while following organizational security requirements. • Apply prompt engineering, structured outputs, and contextual grounding to improve the accuracy and consistency of LLM-generated results. • Implement AI guardrails covering data privacy, confidential information, prompt injection, hallucination, toxicity, and unsafe output . • Establish human-in-the-loop review and approval controls for finance-related AI recommendations and outputs. • Evaluate AI solutions using appropriate measures such as groundedness, faithfulness, relevance, accuracy, latency, and cost . • Monitor implemented models for performance degradation, data drift, unexpected outputs, and changing business conditions . • Explain model results and key drivers using interpretable techniques such as feature importance or SHAP , where appropriate. • Maintain documentation covering model assumptions, training data, validation results, limitations, controls, and implementation decisions . • Develop reusable and maintainable Python code using appropriate software-development and version-control practices . • Use GitHub for source-code management, version control, documentation, issue tracking, and collaboration. • Use GitHub Copilot for code development, debugging, test-case generation, refactoring, and documentation, while independently validating generated code. • Partner with Finance, Data Science, Engineering, Information Security, and Governance teams to move suitable solutions from prototype to controlled business use . AI/ML Implementation Experience Candidates should be able to demonstrate one or more completed implementations, such as financial forecasting or predictive analytics, revenue/bookings/margin/pipeline/c