Data Scientist - Advanced Analytics

• Agentic AI Development and Orchestration: Experience in designing, developing, and orchestrating Agentic AI solutions using autonomous AI agents, workflows, and LLM frameworks. Proficiency in integrating AI agents with enterprise applications, APIs, and data platforms to automate complex business processes. Strong understanding of Retrieval-Augmented Generation (RAG), prompt engineering, guradrails and AI governance best practices.
• Develop Predictive Models: Apply proficiency in programming languages, particularly Python, to design and implement traditional predictive models that forecast trends and suggest optimizations for improved business outcomes.
• Analyze and Interpret Data: Utilize mathematical optimization, discrete-event simulation, and predictive analytics to extract insights from diverse data types and structures, ensuring data-driven decision-making and business optimization.
• Manipulate and Visualize Data: Employ expertise in data manipulation using tools such as Pandas, NumPy, and Dask, and data visualization with Matplotlib, Seaborn, and Plotly to effectively communicate insights to stakeholders.

Preferred Education

Master's Degree

Required Technical And Professional Expertise

• Microsoft Foundry, Copilot and Azure AI services: Strong knowledge of Microsoft Foundry, Microsoft Copilot, and Azure AI Services for developing intelligent applications and copilots. Experience with Azure OpenAI, AI Search, Prompt Flow, and responsible AI practices is preferred. Additional experience with PowerApps will be further preferred.
• Generative AI and Agentic AI: Knowledge of modern Generative AI and Agentic AI frameworks, such as LangChain, LangGraph, Hugging Face, LlamaIndex, CrewAI, and AutoGen, for building intelligent AI applications. Ability to design, orchestrate, and deploy scalable multi-agent workflows with tool integration, memory, Retrieval-Augmented Generation (RAG), and LLM-based reasoning capabilities.
• Programming Languages: Exposure to programming languages, particularly Python, and development environments like PyCharm, VS Code, and Jupyter Notebooks.

Preferred Technical And Professional Experience

• Machine Learning Knowledge: Exposure to machine learning concepts and techniques, including statistical modeling and custom models in applications like supply chain management, pricing, risk assessment, and fraud detection.
• Statistical Analysis Skills: Familiarity with statistical analysis tools like SPSS, SAS, and R, in addition to Python, to analyze and interpret complex data sets.
• Data Manipulation and Visualization: Experience working with data manipulation tools such as Pandas, NumPy, and Dask, and data visualization with Matplotlib, Seaborn, and Plotly.