Technical Specialist-Data Engg

Area(s) of responsibility

3. 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 natural language 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.