IT_Data Analytics_1

1. Statistical Forecasting

• Develop and maintain time-series forecasting models (ARIMA, SARIMA, ETS, Prophet, Theta, ML-based forecasting,exogenous variable modelling etc.).
• Perform trend, seasonality, and variance analysis to improve forecast accuracy.
• Build demand forecasting, sales forecasting, or operational forecasting models for business planning.
• Collaborate with business units to incorporate domain signals into forecasting logic.

2. Regression & Predictive Modelling

• Build and validate regression models (linear, logistic, regularized models such as Lasso/Ridge/ElasticNet).
• Conduct multivariate analysis, hypothesis testing, and variable selection.
• Ensure diagnostic checks (multicollinearity, residual analysis, heteroscedasticity, model fit KPIs).

3. Classification & Machine Learning

• Develop classification models (Random Forests, XGBoost, SVM, Gradient Boosting, Neural Networks).
• Perform model training, validation, hyperparameter tuning, and cross-validation.
• Build end-to-end ML pipelines including data preprocessing, feature engineering, and model deployment.

4. Data Management & Analytics

• Extract, clean, transform, and analyze large structured and unstructured datasets.
• Use SQL, Python, R, or Spark for data manipulation and wrangling.
• Build analytical dashboards or presentations for leadership consumption.

5. Business Problem Solving

• Translate ambiguous business problems into structured analytical frameworks.
• Present complex analytical findings in simple, business-friendly language.
• Work with cross-functional teams to support data-driven decision making.

Statistical Forecasting

• Develop and maintain time-series forecasting models (ARIMA, SARIMA, ETS, Prophet, Theta, ML-based forecasting,exogenous variable modelling etc.).
• Perform trend, seasonality, and variance analysis to improve forecast accuracy.
• Build demand forecasting, sales forecasting, or operational forecasting models for business planning.
• Collaborate with business units to incorporate domain signals into forecasting logic.

2. Regression & Predictive Modelling

• Build and validate regression models (linear, logistic, regularized models such as Lasso/Ridge/ElasticNet).
• Conduct multivariate analysis, hypothesis testing, and variable selection.
• Ensure diagnostic checks (multicollinearity, residual analysis, heteroscedasticity, model fit KPIs).

3. Classification & Machine Learning

• Develop classification models (Random Forests, XGBoost, SVM, Gradient Boosting, Neural Networks).
• Perform model training, validation, hyperparameter tuning, and cross-validation.
• Build end-to-end ML pipelines including data preprocessing, feature engineering, and model deployment.

4. Data Management & Analytics

• Extract, clean, transform, and analyze large structured and unstructured datasets.
• Use SQL, Python, R, or Spark for data manipulation and wrangling.
• Build analytical dashboards or presentations for leadership consumption.

5. Business Problem Solving

• Translate ambiguous business problems into structured analytical frameworks.
• Present complex analytical findings in simple, business-friendly language.
• Work with cross-functional teams to support data-driven decision making.

• Bachelor’s or Master’s degree in Statistics, Mathematics, Economics, Engineering, Computer Science, or Data Science.
• 8-15 years of relevant experience in statistical forecasting, predictive analytics, or data science roles.
• Experience in manufacturing or/and retail is a plus.
• Exposure to cloud environments (Azure, AWS, GCP).
• Experience with MLOps, model monitoring, and versioning (MLflow, Git).
• Knowledge of NLP or deep learning is an advantage.
• Prior experience in building automated forecasting/propensity/Deep Learning pipelines.
• Strong experience in Python/Pyspark and/or R (pandas, NumPy, scikit-learn, statsmodels, tidyverse).
• Hands-on experience with time-series forecasting techniques.
• Familiarity with classification algorithms and ensemble methods.
• Strong understanding of statistics: distributions, probability, ANOVA, hypothesis testing.
• Practical experience in SQL and working with large datasets (preferably Spark / Databricks).
• Experience with visualization tools (Power BI, Tableau, matplotlib, seaborn).