Key Responsibilities
Statistical Analysis & Experimentation
- Perform hypothesis testing using T-Test, Z-Test, and other statistical methods
- Apply regression techniques (linear and logistic) for predictive modeling
- Conduct classification modeling using Decision Trees and SVM
- Use probabilistic graph models for advanced analytical insights
Machine Learning Model Development
- Build and deploy ML models using frameworks like TensorFlow, PyTorch, Scikit-learn, Keras, MXNet, and CNTK
- Perform forecasting using ARIMA, ARIMAX, and Exponential Smoothing techniques
- Apply advanced distance metrics such as Euclidean, Manhattan, and Hamming distance in model design
Data Engineering & Processing
- Develop scalable data pipelines using Python and PySpark
- Perform statistical computing using SAS, SPSS, and R/R Studio
- Ensure high-quality data processing and transformation for analytics and ML workflows
Data Quality & Monitoring
- Implement data validation frameworks using Great Expectations
- Monitor model performance and data drift using Evidently AI
- Ensure reliability, consistency, and accuracy of data pipelines
Agentic AI & Advanced AI Systems
- Contribute to the development of Agentic AI systems for autonomous decision-making
- Work with orchestration and deployment tools such as Kubeflow and BentoML
- Support end-to-end ML lifecycle management including deployment and monitoring
Forecasting & Advanced Analytics
- Build time-series forecasting models for business and operational insights
- Apply advanced statistical and probabilistic techniques for decision support
- Translate analytical outputs into actionable insights for stakeholders
Collaboration & Problem Solving
- Work closely with data science and engineering teams to solve complex business problems
- Support cross-functional teams in adopting AI-driven solutions
- Communicate analytical findings clearly to technical and non-technical stakeholders
