• Conduct in-depth analysis of large structured and unstructured datasets to uncover patterns, insights, and signals that inform AI models, knowledge representations, and product decisions. • Develop and apply statistical methods to identify patterns, trends, and correlations in HR performance, engagement, and behavioral data, supporting evidence-based insights and model development. • Design and engineer features from diverse data sources (e.g., reviews, surveys, behavioral signals, organizational data) to power machine learning models and construct structured AI context layers and knowledge representations used by intelligent systems and agents. • Lead the creation and curation of high-quality labeled datasets, including annotation frameworks, evaluation datasets, and ground truth benchmarks that support model training and validation. • Design, develop, and train machine learning models (supervised, unsupervised, and representation-learning approaches) for use cases that enhance customer outcomes and enable intelligent product capabilities. • Develop data structures, embeddings, and context-enrichment pipelines that enable AI agents to retrieve relevant organizational knowledge, behavioral insights, and product context. • Partner with engineering teams to operationalize models in production environments, ensuring scalability, reliability, and alignment with product architecture. • Design evaluation frameworks and continuously monitor model performance, iterating on models, data pipelines, and features based on experimentation, feedback, and evolving business needs. • Work closely with product, engineering, and design teams to translate business problems into data science solutions and ensure models deliver measurable value. • Maintain clear documentation for datasets, modeling approaches, evaluation frameworks, and data pipelines to ensure reproducibility, transparency, and knowledge transfer across teams. • Design evaluation datasets and benchmarking frameworks to measure AI agent performance, including retrieval quality, reasoning accuracy, and contextual relevance. • Ensure that data used in models and AI systems meets privacy, security, and ethical standards, particularly when working with sensitive employee performance and engagement data.