Build and scale autonomous AI systems and enterprise copilots using Microsoft 365 and Azure technologies, while mentoring teams and driving AI innovation.
Principal Data Scientist
Primary Skills
A/B Testing, Clustering, AWS, Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Parameter Tuning, Data Wrangling, Exploratory Data Analysis, Python/PySpark, PCA, Factor Analysis, SAS/SPSS, GitHub / Gitlab, IBM Watson, Feature Engineering, Tree Aglorithms, Grid Search, SVM, Tools(KubeFlow, BentoML), Cross Validation, Data Curiosity, Design Thinking, Data Literacy
Job requirements
This role is explicitly focused on building agentic systems from scratch, including the design and development of autonomous AI agents and enterprise copilots, with hands-on experience in Microsoft 365 Copilot and Copilot Studio ecosystems as a core requirement. ________________________________________ Core Responsibilities • Agentic AI Architecture & Engineering: Build AI agents and multi-agent systems from scratch, including agent orchestration, autonomy design, tool-use, memory management, and decision frameworks. • Enterprise Copilot Development: Design, build, and scale enterprise copilots using Microsoft Copilot Studio and Microsoft 365 Copilot, enabling intelligent task automation and knowledge workflows. • Build AI agents & copilots on Microsoft Copilot Studio • Develop automated workflows using Power Automate / Logic Apps • Integrate AI agents with enterprise systems via APIs, connectors, and Azure services • Implement RAG, grounding, semantic search using Azure OpenAI & Azure Cognitive Search • Ensure security, governance, and responsible AI practices • Collaborate with architects, analysts, and business teams • Problem Formulation: Translate business objectives into well-defined data science, ML, and Agentic AI problems; validate OKRs using robust statistical and experimental measures. • Agentic AI & LLM Solutions: Design, build, deploy, and optimize Agentic AI systems (multi-agent workflows, task orchestration, autonomous decision-making) using LLMs for real-world enterprise use cases. • LLM Development & Deployment: Fine-tune, prompt-engineer, evaluate, and productionize LLMs (open-source or proprietary) for use cases such as copilots, RAG pipelines, conversational AI, and intelligent automation. • Data Wrangling & Feature Engineering: Handle structured and unstructured data at scale, including text, documents, and conversational data for LLM-powered solutions. • Insight Generation & Data Storytelling: Convert complex analytical outputs and AI model results into clear, compelling narratives for business and executive audiences. • Technical Decision-Making: Make informed trade-offs on model complexity, iteration depth, experimentation cycles, and time-to-value. • Design Thinking & Innovation: Apply design thinking principles to build user-centric AI products and data solutions. • Mentorship & Leadership: Coach senior data scientists, review architectures, and establish best practices across data science, ML, and GenAI initiatives.
