Data Scientist

As a Data Scientist at our company, you will play a crucial role in building and implementing machine learning solutions for real-world industrial applications. Your responsibilities will include: - Building and evaluating predictive models such as regression, classification, and time series forecasting to support business decisions and operational optimization. - Developing end-to-end modeling workflows on Azure, including model training, validation, and deployment. - Working within Databricks-based environments to perform data exploration, feature engineering, model training, and leveraging MLflow for experiment tracking and model lifecycle management. - Supporting scalable data pipelines and batch/real-time inference workflows. - Implementing and adhering to MLOps standards including model versioning, CI/CD integration, reproducibility, monitoring model performance, and supporting deployment through containerized or API-based solutions. - Collaborating with data engineers, domain experts, and IT teams to translate business requirements into technical solutions, ensure data quality, governance, production reliability, and contribute to standardized ML lifecycle practices. Qualifications required for this role include: - Bachelors degree in Data Science, Statistics, Computer Science, Engineering, or a related field. - Minimum 2 years of relevant work experience in data science or machine learning. - Strong proficiency in Python and ML libraries such as scikit-learn, XGBoost. - Experience with Azure cloud services, Azure Databricks, Azure ML, Data Lake, CI/CD pipelines, or equivalent platforms like AWS/GCP. - Hands-on experience with Databricks and familiarity with MLOps practices. - Experience with SQL and working on large-scale datasets. Preferred qualifications include: - Advanced degree. - Experience in domains like Preventative Maintenance/Equipment Reliability, Process Optimization in manufacturing or operations, Quality Control, anomaly detection systems, or Inventory optimization/supply chain analytics. - Exposure to cloud platforms, especially Azure, and production ML deployment patterns. You will be a valuable asset to our team with your knowledge of the end-to-end ML lifecycle. As a Data Scientist at our company, you will play a crucial role in building and implementing machine learning solutions for real-world industrial applications. Your responsibilities will include: - Building and evaluating predictive models such as regression, classification, and time series forecasting to support business decisions and operational optimization. - Developing end-to-end modeling workflows on Azure, including model training, validation, and deployment. - Working within Databricks-based environments to perform data exploration, feature engineering, model training, and leveraging MLflow for experiment tracking and model lifecycle management. - Supporting scalable data pipelines and batch/real-time inference workflows. - Implementing and adhering to MLOps standards including model versioning, CI/CD integration, reproducibility, monitoring model performance, and supporting deployment through containerized or API-based solutions. - Collaborating with data engineers, domain experts, and IT teams to translate business requirements into technical solutions, ensure data quality, governance, production reliability, and contribute to standardized ML lifecycle practices. Qualifications required for this role include: - Bachelors degree in Data Science, Statistics, Computer Science, Engineering, or a related field. - Minimum 2 years of relevant work experience in data science or machine learning. - Strong proficiency in Python and ML libraries such as scikit-learn, XGBoost. - Experience with Azure cloud services, Azure Databricks, Azure ML, Data Lake, CI/CD pipelines, or equivalent platforms like AWS/GCP. - Hands-on experience with Databricks and familiarity with MLOps practices. - Experience with SQL and working on large-scale datasets. Preferred qualifications include: - Advanced degree. - Experience in domains like Preventative Maintenance/Equipment Reliability, Process Optimization in manufacturing or operations, Quality Control, anomaly detection systems, or Inventory optimization/supply chain analytics. - Exposure to cloud platforms, especially Azure, and production ML deployment patterns. You will be a valuable asset to our team with your knowledge of the end-to-end ML lifecycle.