About the Role
Job Description: Requirements:
5+ years of hands-on experience in applied machine learning, with a focus on regression, forecasting, or optimization. Proven experience in production-grade ML pipelines, from experimentation to deployment. Strong grasp of data science concepts such as cross-validation, quantile modeling, and model safeguard techniques. Strong background in Python and data science libraries, with the ability to write clean, efficient, and production-ready code. Experience on Git and related workflows, code review processes, automated testing, and CI/CD pipelines. Solid understanding of data lifecycle management, including time-series feature engineering and retraining strategies. Experience with ML model monitoring, versioning, and continuous retraining frameworks. Familiarity with cloud ML ecosystems (Azure or AWS). Experience with Azure ML, CosmosDB, Serv...