IT_Data Analytics_1 (100)

Posted 5mo ago

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.

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).

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.

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.

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.

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).

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.

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.

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).

Skills