Data Scientist - LLM / Machine Learning

Key Responsibilities Design, develop, and deploy machine learning models that power autonomous functionality within our cloud- based operations management platform. Conduct on-site visits to observe real-world system environments and evaluate model performance in action. Collaborate closely with cross-functional teams, including product management and engineering, to define requirements and integrate ML solutions into production systems. Conduct exploratory data analysis for model development and optimization. Continuously evaluate model performance and implement improvements to enhance accuracy, reliability, and scalability. Stay current with advancements in machine learning research and tools, applying relevant innovations to real- world challenges. Build and maintain robust data pipelines and workflows to enable training and deployment of ML models in a cloud-native environment. Required Qualifications Masters in Computer Science, Data Science, Artificial Intelligence, or a related field, OR equivalent experience. PhD is a plus. Demonstrated proficiency in Python and relevant ML libraries (e.g., PyTorch, Scikit-learn). Strong understanding of machine learning algorithms, statistical modeling, and data structures. Solid knowledge of data engineering concepts and experience working with large-scale, real world datasets. Proven ability to turn complex problems into practical, deployable solutions. Excellent communication and collaboration skills, with the ability to explain technical concepts to non-technical stakeholders. Preferred Experience deploying machine learning models in cloud environments (e.g., AWS, Azure, or Google Cloud). Experience with developing LLMs and agentic AI. Familiarity with tabular and sparse data. Exposure to time-series data or anomaly detection use cases. Familiarity with online learning and other ML techniques. Contributions to open-source projects, published research in machine learning or applied data science, or relevant personal projects. Familiarity with MLOps best practices, including model monitoring, drift detection, and retraining pipelines. Prior experience working on B2B SaaS platforms operational technology systems. Key Responsibilities Design, develop, and deploy machine learning models that power autonomous functionality within our cloud- based operations management platform. Conduct on-site visits to observe real-world system environments and evaluate model performance in action. Collaborate closely with cross-functional teams, including product management and engineering, to define requirements and integrate ML solutions into production systems. Conduct exploratory data analysis for model development and optimization. Continuously evaluate model performance and implement improvements to enhance accuracy, reliability, and scalability. Stay current with advancements in machine learning research and tools, applying relevant innovations to real- world challenges. Build and maintain robust data pipelines and workflows to enable training and deployment of ML models in a cloud-native environment. Required Qualifications Masters in Computer Science, Data Science, Artificial Intelligence, or a related field, OR equivalent experience. PhD is a plus. Demonstrated proficiency in Python and relevant ML libraries (e.g., PyTorch, Scikit-learn). Strong understanding of machine learning algorithms, statistical modeling, and data structures. Solid knowledge of data engineering concepts and experience working with large-scale, real world datasets. Proven ability to turn complex problems into practical, deployable solutions. Excellent communication and collaboration skills, with the ability to explain technical concepts to non-technical stakeholders. Preferred Experience deploying machine learning models in cloud environments (e.g., AWS, Azure, or Google Cloud). Experience with developing LLMs and agentic AI. Familiarity with tabular and sparse data. Exposure to time-series data or anomaly detection use cases. Familiarity with online learning and other ML techniques. Contributions to open-source projects, published research in machine learning or applied data science, or relevant personal projects. Familiarity with MLOps best practices, including model monitoring, drift detection, and retraining pipelines. Prior experience working on B2B SaaS platforms operational technology systems.