Overview
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. 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 regulat
Full job description
Full Job Description
- 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. 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. Design and develop AI-native experiences leveraging Large Language Models (LLMs), AI agents, copilots, retrieval systems, and autonomous workflows. Establish evaluation-driven development methodologies that continuously measure quality, relevance, usefulness, safety, and business impact. Provide technical guidance to engineers and drive best practices for MLOps within the team. Ensure that the entire ML lifecycle adheres to privacy and compliance requirements (e.g., GDPR, CCPA). 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. 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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