Senior Staff Engineer, Machine Learning

Summary

Join our team as a Machine Learning Engineer with 8+ years of IT experience and strong skills in Python, Machine Learning on Azure, SQL, model deployment and monitoring, and MLOps. You will develop and implement machine learning models to solve complex business problems, deploy models to production environments, and monitor their performance. Responsibilities include maintaining models, developing ethical use policies, designing Python libraries, maintaining data science assets, creating documentation, participating in demos, and identifying areas for pipeline improvement. A strong understanding of statistical analysis and modeling techniques is essential. Experience in MLOps is required, and knowledge of deep learning fundamentals is preferred.

Requirements

• Strong understanding of statistical analysis and modelling techniques (e.g., regression analysis, hypothesis testing, time series analysis)
• Must have 8+ years of IT experience
• Must have experience in MLOps
• Python (Strong)
• Machine Learning on Azure (Strong)
• SQL (Strong)
• Model Deployment and Monitoring (Strong)

Responsibilities

• Develop and implement machine learning models to solve complex business problems
• Deploy machine learning models to production environments, ensuring that models are integrated with software systems
• Monitor machine learning models in production, ensuring that models are performing as expected and that any errors or performance issues are identified and resolved quickly
• Maintain machine learning models over time. This includes updating models as new data becomes available, retraining models to improve performance, and retiring models that are no longer effective
• Develop and implement policies and procedures to ensure the ethical and responsible use of machine learning models. This includes addressing issues related to bias, fairness, transparency, and accountability
• Design, contribute, and participate in the implementation of Python libraries answering a data science transversal need that can be reused in several projects
• Maintain existing data science assets (timeseries forecasting asset, model monitoring asset)
• Create documentation and knowledge based on data science assets to ensure users understand them well
• Participate in asset demos to showcase new features to users
• Proactively identify areas of improvement within the pipelines

Preferred Qualifications

Good to have knowledge of Deep Learning fundamentals