Key Responsibilities
Forecasting & Predictive Modeling
- Develop and implement time series forecasting models for demand, trend, and performance prediction
- Apply statistical and machine learning techniques such as ARIMA, SARIMA, ETS, Prophet, and LSTM models
- Use regression techniques (linear, logistic) and classification algorithms (decision trees, SVM) for predictive analytics
- Leverage probabilistic and statistical models to improve forecasting accuracy
Data Preparation & Feature Engineering
- Work with large, multi-source datasets for cleaning, transformation, and preparation
- Perform data validation, anomaly detection, and ensure high data quality and integrity
- Apply distance metrics such as Euclidean, Manhattan, and Hamming distance where applicable
Statistical Analysis & Hypothesis Testing
- Conduct hypothesis testing using T-tests, Z-tests, and other statistical methods
- Perform deep statistical analysis to identify trends, correlations, and business drivers
- Support decision-making through data-driven insights and modeling outputs
Machine Learning & AI Model Development
- Build and deploy ML models using frameworks such as Scikit-learn, TensorFlow, PyTorch, Keras, MXNet, and CNTK
- Work with probabilistic graphical models and advanced ML techniques
- Optimize models through experimentation and performance tuning
MLOps, Deployment & Tools
- Use tools such as Kubeflow and BentoML for model deployment and lifecycle management
- Ensure model monitoring, validation, and continuous improvement using tools like Evidently AI and Great Expectations
- Support reproducibility through structured documentation and scalable pipelines
Collaboration & Business Impact
- Collaborate with business and technical teams to understand forecasting requirements
- Translate analytical outputs into actionable insights for supply chain, demand planning, and operations
- Communicate findings effectively to both technical and non-technical stakeholders
Optimization & Continuous Improvement
- Continuously improve forecasting models by testing new algorithms and techniques
- Experiment with deep learning and statistical innovations for better accuracy
- Maintain scalable, efficient, and reusable codebases for analytics solutions
