IT_Data Analytics_1 (100)

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).