Seeking a skilled Data Science AI Engineer to spearhead the design, development, and productionization of advanced AI/ML models for financial forecasting. This role involves analyzing historical data to uncover predictive features and business patterns.

Key responsibilities include developing sophisticated time-series forecasting models like Prophet, ARIMA, LSTM, and XGBoost. You will also build a Conversational AI layer using Azure OpenAI and RAG patterns to enable natural language querying of financial and workforce data.

Collaboration with FP&A SMEs is crucial for translating business drivers into model features and interpretable outputs. The role emphasizes delivering explainable AI outputs (SHAP, feature importance) to foster trust and drive action from Finance users. You will partner with Sr. Application Developers on MLOps, model monitoring, and retraining strategies. Comprehensive documentation of experiments, model cards, and limitations is required for governance.

Design, develop, validate, and productionize AI/ML models for financial forecasting. Analyze historical data to identify predictive features and business-relevant patterns. Develop time-series forecasting models (Prophet, ARIMA, LSTM, XGBoost) and quantify forecast accuracy. Build a Conversational AI layer using Azure OpenAI + RAG for natural language queries. Partner with FP&A SMEs to translate business drivers into model features. Deliver explainable AI outputs (SHAP, feature importance) for stakeholder trust. Collaborate on operationalizing models via MLOps pipelines, monitoring, and retraining. Document experiments, model cards, assumptions, and limitations.

A minimum of 5 years of hands-on experience with Python for Data Science, including pandas, NumPy, scikit-learn, statsmodels, and Jupyter. Deep expertise in time-series forecasting models such as Prophet, ARIMA/SARIMA, exponential smoothing, XGBoost/LightGBM, and deep learning (LSTM/Transformer). Proficiency in statistical modeling techniques including regression, classification, clustering, feature engineering, hyperparameter tuning, cross-validation, and model evaluation. Hands-on experience with the Azure ML Stack, encompassing Azure Machine Learning (AML), MLflow, Azure OpenAI, and model deployment. Practical experience with LLMs and GenAI, including prompt engineering, RAG, embeddings, vector stores (Azure AI Search, FAISS, Pinecone), and frameworks like LangChain or Semantic Kernel. Strong understanding of explainability techniques (SHAP, LIME, partial dependence plots) and communicating model behavior to non-technical audiences. Prior experience with Finance/FP&A/Corporate Planning use cases is preferred, as is exposure to Energy, Oil & Gas, or EPC project economics.