Principal Machine Learning Scientist

Key Responsibilities: 

1. ML/AI Strategy & Technical Leadership: 

• Define and drive the scientific vision for AI and machine learning initiatives across CEAI. 

• Influence technical strategy and roadmap decisions across multiple product and platform teams. 

• Serve as a trusted advisor to engineering, product, and executive leadership. 

• Lead cross-organizational initiatives involving AI platforms, model development, evaluation systems, and business intelligence capabilities. 

• Mentor scientists and engineers while establishing scientific and engineering best practices. 

1. Model Operationalization: 

• Implement CI/CD pipelines for ML models, ensuring smooth deployments with minimal downtime. 

• Design and deploy robust monitoring and alerting systems for ML models in production to detect issues such as model drift or data skew. 

• Implement model governance, version control, and logging systems to ensure compliance with internal standards and external regulations. 

1. AI-Native AI/ML Development: 

• Design and develop AI-native experiences leveraging Large Language Models (LLMs), AI agents, copilots, retrieval systems, and autonomous workflows. 

• Lead development of agentic systems utilizing tool orchestration, memory architectures, reasoning frameworks, and multi-agent collaboration. 

• Drive adoption of AI-assisted and AI-native ML development practices. 

• Establish evaluation-driven development methodologies that continuously measure quality, relevance, usefulness, safety, and business impact. 

1. Operational Excellence: 

• Partner with cross-functional teams, including Data Engineering, Product Management, and other Engineering teams to build cohesive solutions. 

• Provide technical guidance to engineers and drive best practices for MLOps within the team. 

• Partner with engineering teams to productionize ML/AI solutions at MCAPs at scale 

• Drive adoption of modern ML practices. 

1. Security & Compliance: 

• Work on securing models, data pipelines, and infrastructure in compliance with Microsoft's security standards. 

• Ensure that the entire ML lifecycle adheres to privacy and compliance requirements (e.g., GDPR, CCPA). 

Required/minimum qualifications

Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 7+ years data science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

Preferred Qualifications

• 10+ years developing and deploying machine learning solutions in production. 

• 5+ years leading large-scale AI or machine learning initiatives. 

• Experience delivering measurable business impact through applied science. 

• Experience in machine learning, deep learning, statistical modeling, and experimentation. 

• Experience with Large Language Models and Generative AI technologies. 

• Experience building production AI systems using Azure AI, Azure OpenAI, Azure Machine Learning, or similar platforms. 

• Proficiency in Python and modern ML frameworks including PyTorch, TensorFlow, Hugging Face, Semantic Kernel, AutoGen, LangGraph, or equivalent. 

• Experience designing and evaluating agentic AI systems and AI-native applications. 

• Experience with MLOps, LLMOps, model governance, and AI observability. 

• Experience building enterprise copilots, AI agents, or autonomous workflows at scale. 

• Experience with AI evaluation platforms, benchmarking systems, and model quality measurement frameworks. 

• Experience with  Sales Intelligence, Commercial Operations, Customer Success, Commerce Systems, Business Applications, Revenue Optimization, Customer Insights 

• Experience patents, publications, open-source leadership, or notable technical innovation. 

• Experience leading AI transformation initiatives within large organizations. 

• AI-native operating models and human-AI collaboration patterns experience. 

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