Country/Region:
IN
Requisition ID:
39434
Work Model:
Position Type:
Salary Range:
Location:
INDIA - PUNE - BIRLASOFT OFFICE - HINJAWADI
Title: Technical Specialist-Data Engg
Description:
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 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.