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.
Responsibilities
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).
Redefining the modern homes with innovation and Excellence Havells is at the forefront of transforming modern living spaces by offering innovative solutions that elevate everyday experiences. With a deep understanding of consumer needs, the company has expanded its footprint across every aspect of home life, creating environment that are not only functional but also comfortable, stylish, and future-ready. To make these solutions accessible, Havells leverages an extensive distribution network encompassing 18,000 dealers, over 1,000 exclusive brand stores, intensifying presence in modern retail, e-commerce and quick commerce. The company’s portfolio includes power brands like Havells, Havells Crabtree, Lloyd, REO, Havells Studio, and Standard each designed to cater to the unique demands of today’s consumers. Anchored in the ‘Make in India’ initiative, Havells operates 16 cutting-edge manufacturing facilities across India, producing 90% of its products in-house. At the forefront of Havells product evolution lies its Customer Experience & Designs (CXD) Studio, where creativity and user-centric design converge. This is complemented by its Centre for Research & Innovation (CRI), supported by four advanced R&D centres, enabling Havells to consistently deliver products that are both functional and visually compelling. Driven by a commitment to innovation, quality, and design, the company also upholds sustainable practices throughout its operations, including eco-friendly manufacturing processes and energy-efficient product design, contributing to a greener and more sustainable future.
Skills Required
- 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
- Experience in manufacturing and/or retail
- Exposure to cloud environments (Azure, AWS, GCP)
- Experience with MLOps, model monitoring, and versioning (MLflow, Git)
- Knowledge of NLP or deep learning
- Prior experience building automated forecasting, propensity, or 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 (ARIMA, SARIMA, ETS, Prophet, Theta, ML-based forecasting)
- Familiarity with classification algorithms and ensemble methods (Random Forests, XGBoost, SVM, Gradient Boosting)
- 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)
