Data Scientist

As a Data Scientist at our company, you will be responsible for delivering production-grade analytics solutions in a fast-paced, agile environment. Your focus will be on supply chain and commercial analytics. You will work across the full ML lifecycle from data exploration to model deployment. Key Responsibilities: - Build, deploy, and maintain ML models end-to-end using Azure Databricks and Azure Pipelines - Write production-quality code in Python/PySpark and actively contribute to team repositories - Design and scale data pipelines (batch and real-time) using big data technologies - Collaborate with data and ML engineers on model industrialization and deployment automation - Translate business problems into modeling solutions and effectively communicate insights to stakeholders - Stay updated with the latest ML/AI methodologies, create reusable libraries, and maintain documentation Must-Have Skills: - Strong hands-on proficiency in Python, PySpark, and SQL - Experience in supervised & unsupervised ML regression, classification, and clustering - Knowledge of applied statistics including distributions, hypothesis testing, and regression - Proficiency in Git, CI/CD workflows, and version control best practices - Experience with Azure cloud services such as Databricks and ADF - Familiarity with MLOps tools like MLflow, Kubeflow, or equivalent Good to Have: - Experience in Time Series analysis and Demand Forecasting - Knowledge of NLP, Bayesian methods, Causal Inference, and Reinforcement Learning - Familiarity with Docker, Jenkins, and big data technologies like Spark/Hive - Understanding of Responsible AI and Distributed ML - Previous experience in the supply chain or retail domain Qualifications: - Bachelor's degree in Computer Science, Mathematics, or a related field - Minimum of 4 years of experience as a Data Scientist in a production environment - Preferred experience in supply chain or commercial analytics - Agile team delivery experience is a plus (Note: The additional details of the company were not included in the provided job description.) As a Data Scientist at our company, you will be responsible for delivering production-grade analytics solutions in a fast-paced, agile environment. Your focus will be on supply chain and commercial analytics. You will work across the full ML lifecycle from data exploration to model deployment. Key Responsibilities: - Build, deploy, and maintain ML models end-to-end using Azure Databricks and Azure Pipelines - Write production-quality code in Python/PySpark and actively contribute to team repositories - Design and scale data pipelines (batch and real-time) using big data technologies - Collaborate with data and ML engineers on model industrialization and deployment automation - Translate business problems into modeling solutions and effectively communicate insights to stakeholders - Stay updated with the latest ML/AI methodologies, create reusable libraries, and maintain documentation Must-Have Skills: - Strong hands-on proficiency in Python, PySpark, and SQL - Experience in supervised & unsupervised ML regression, classification, and clustering - Knowledge of applied statistics including distributions, hypothesis testing, and regression - Proficiency in Git, CI/CD workflows, and version control best practices - Experience with Azure cloud services such as Databricks and ADF - Familiarity with MLOps tools like MLflow, Kubeflow, or equivalent Good to Have: - Experience in Time Series analysis and Demand Forecasting - Knowledge of NLP, Bayesian methods, Causal Inference, and Reinforcement Learning - Familiarity with Docker, Jenkins, and big data technologies like Spark/Hive - Understanding of Responsible AI and Distributed ML - Previous experience in the supply chain or retail domain Qualifications: - Bachelor's degree in Computer Science, Mathematics, or a related field - Minimum of 4 years of experience as a Data Scientist in a production environment - Preferred experience in supply chain or commercial analytics - Agile team delivery experience is a plus (Note: The additional details of the company were not included in the provided job description.)