Technical Specialist-Data Engg

Area(s) of responsibility

Data Scientist (Technical Specialist) Specialist Grade

Experience: 6–8 years · Location: [To be filled] · Engagement: Full-time

Role Expectations

- Own the design, development, validation, and productionization of 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) tailored to project and quantify forecast accuracy against agreed thresholds.

- Build a Conversational AI layer using Azure OpenAI + RAG patterns to enable naturallanguage queries over financial and workforce data.

- Partner with FP&A SMEs to translate business drivers into model features and interpretable outputs.

Reports To

Senior Technical Lead (Python) / Delivery Manager

Grade

Technical Specialist (above Sr. App Developer, below Sr. Technical Lead)

Program

FP&A Reporting Automation & AI Forecasting

- Deliver explainable AI outputs (SHAP, feature importance, etc.) so that Finance users can trust and act on model recommendations.

- Collaborate with the Sr. Application Developer to operationalize models — MLOps pipelines, model registry, monitoring, and retraining triggers.

- Document experiments, model cards, assumptions, and limitations for audit and governance review. Must-Have Skills

- Python for Data Science: 5+ years hands-on with pandas, NumPy, scikit-learn, statsmodels, and Jupyter-based experimentation.

- Time-Series Forecasting: Deep experience with Prophet, ARIMA/SARIMA, exponential smoothing, XGBoost/LightGBM, and deep learning (LSTM/Transformer) for financial or operational forecasting.

- Statistical Modeling: Regression, classification, clustering, feature engineering, hyperparameter tuning, cross-validation, and rigorous model evaluation.

- Azure ML Stack: Hands-on with Azure Machine Learning (AML), MLflow, Azure OpenAI, and model deployment as endpoints or batch jobs.

- LLM & GenAI: Practical experience with prompt engineering, RAG (Retrieval-Augmented Generation), embeddings, vector stores (Azure AI Search / FAISS / Pinecone), and LangChain or Semantic Kernel.

- Explainability: SHAP, LIME, partial dependence plots, and communicating model behavior to non-technical Finance stakeholders.

Preferred / Good-to-Have Skills

- Prior experience with Finance / FP&A / Corporate Planning use cases.

• Exposure to Energy, Oil & Gas, or EPC project economics

• Exposure to agent frameworks (LangGraph, AutoGen, Semantic Kernel) for building autonomous analytical agents.