Key Responsibilities:
Machine Learning & Predictive Modeling
- Develop and deploy machine learning models for classification, regression, and forecasting.
- Apply algorithms such as Decision Trees, SVM, Linear/Logistic Regression, and ARIMA/ARIMAX.
- Work on probabilistic models and graph-based learning approaches.
Statistical Analysis & Experimentation
- Perform hypothesis testing using T-Test, Z-Test, and other statistical methods.
- Conduct advanced statistical analysis and interpret results for business decisions.
- Apply distance metrics such as Euclidean, Manhattan, and Hamming distance where required.
Time Series & Forecasting
- Build forecasting models using techniques like Exponential Smoothing and ARIMA.
- Analyze trends and patterns in time-dependent datasets.
Data Quality & Monitoring
- Implement data validation frameworks using tools like Great Expectations.
- Monitor model performance and data drift using Evidently AI.
Programming & Frameworks
- Develop solutions using Python, PySpark, and R.
- Use ML frameworks such as TensorFlow, PyTorch, Scikit-learn, Keras, MXNet, and CNTK.
- Work with SAS/SPSS for statistical computing and analysis.
MLOps & Deployment
- Use tools like Kubeflow and BentoML for model deployment and pipeline automation.
- Support end-to-end ML lifecycle from development to production.
AI & Agentic Systems (Emerging Requirement)
- Work on Agentic AI systems using frameworks such as LangGraph and related tools.
- Contribute to intelligent, autonomous decision-making systems.
