Join our team as a Technical Specialist in Data Engineering and drive the future of financial forecasting. This role focuses on designing, developing, and deploying advanced AI/ML models to enhance financial planning and analysis.

You will leverage historical data to uncover predictive features and critical business patterns. The position involves creating sophisticated time-series forecasting models and building a conversational AI layer for natural language queries over financial data.

This is an exciting opportunity to collaborate with FP&A subject matter experts, translate business needs into interpretable AI outputs, and ensure Finance users can confidently act on model recommendations. You'll also work closely with application developers to operationalize these models through robust MLOps pipelines.

Lead the design, development, validation, and productionization of AI/ML models for financial forecasting. Analyze historical data to identify predictive features and relevant business patterns. Develop and refine time-series forecasting models (e.g., Prophet, ARIMA, LSTM, XGBoost) and quantify forecast accuracy. Construct a conversational AI layer using Azure OpenAI and RAG patterns for natural language querying of financial and workforce data. Collaborate with FP&A SMEs to transform business drivers into model features and interpretable outputs. Deliver explainable AI outputs (SHAP, feature importance) to foster trust and actionability for Finance users. Partner with Sr. Application Developers to operationalize models, implementing MLOps pipelines, model registry, monitoring, and retraining triggers. Document experiments, model cards, assumptions, and limitations for audit and governance reviews.

A strong foundation in Python for Data Science with 5+ years of hands-on experience in pandas, NumPy, scikit-learn, statsmodels, and Jupyter-based experimentation is essential. Demonstrate deep expertise in time-series forecasting techniques, including Prophet, ARIMA/SARIMA, exponential smoothing, XGBoost/LightGBM, and deep learning models (LSTM/Transformer) for financial or operational forecasting. Possess solid statistical modeling skills covering regression, classification, clustering, feature engineering, hyperparameter tuning, cross-validation, and rigorous model evaluation. Exhibit practical experience with the Azure ML Stack, including Azure Machine Learning (AML), MLflow, Azure OpenAI, and deploying models as endpoints or batch jobs. Showcase hands-on experience with LLM & GenAI concepts, prompt engineering, RAG, embeddings, vector stores (Azure AI Search, FAISS, Pinecone), and frameworks like LangChain or Semantic Kernel. Proficiency in explainability techniques such as SHAP, LIME, and partial dependence plots, with the ability to communicate model behavior to non-technical Finance stakeholders.