Experience: 10+ years Job Description: • Must have good experience in implementing machine learning models such as Prophet, ARIMA, SARIMA, XGBoost, ElasticNet, Ridge, Lasso, Random Forest, and Linear Regression on time-series data. • Proficient in using Python ML packages such as scikit-learn, sktime, and darts. • Must have strong expertise in key techniques for time series feature engineering, including lag features, rolling window statistics, Fourier transforms, and handling seasonality. • Proven ability to tune the performance of existing deployed forecasting models. • Must have experience with Azure Machine Learning Python SDK v1/v2 to: • Manage data, models, and environments • Build/debug AML pipelines to stitch together multiple tasks (feature engineering, training, registering models, etc.) and production workflows using Azure ML pipelines • Schedule Azure ML jobs • Deploy registered models to create endpoints. • Good to have experience with K-Means clustering. • Must utilized Azure services such as Azure Data Factory, Azure Databricks, Azure Data Lake, and Azure Key Vault to architect and maintain scalable data solutions. • Design, develop, and deploy new Azure Data Factory (ADF) pipelines for data ingestion, transformation, and logging, ensuring robustness and reliability. • Proficiently transform and manipulate data using PySpark and Python, leveraging their capabilities to derive actionable insights from complex datasets. • Collaborate with cross-functional teams to understand data requirements and translate them into effective technical solutions. • Lead the implementation and optimization of CI/CD pipelines using Azure DevOps, ensuring a seamless build and release flow for data infrastructure and applications. • Drive best practices in data engineering, including data governance, security, and performance optimization. • Stay abreast of industry trends and emerging technologies, contributing to the continuous improvement of our data engineering capabilities.