Data Science Manager

Askuity is a Toronto-based retail analytics software company operating as a division within The Home Depot (THD). Through our supplier analytics program, Askuity's mission is to enable suppliers and merchants at The Home Depot to make profitable, data-driven decisions and drive real-time execution.

As a Manager, Data Science you will lead the development and integration of AI-enabled capabilities within Fusion, The Home Depot's Business Intelligence tool for suppliers. In this role, you will lead a team of data scientists and collaborate with cross-functional stakeholders to enhance decision-making and analytical capabilities within the platform. Your work will be instrumental in optimizing supplier performance, driving insights, and ensuring Home Depot suppliers can leverage data-driven strategies effectively.

Key Responsibilities:

• Lead and Mentor a team of data scientists in developing AI_powered solutions, including GenAI for Fusion
• Define the roadmap and strategy to enhance predictive analytics, machine learning, GenAI capabilities, intelligent automation, and decision support functionalities
• Collaborate with leaders from product management, design, and engineering to integrate advanced analytics into the BI tool
• Design an implement machine learning models, NLP, LLM-powered applications, retrieval-augmented generation (RAG), prompt engineering, and other AI techniques to improve user experience and insights generation
• Ensure data integrity, quality, and security across all analytics processes
• Drive innovation by evaluating emerging AI technologies including foundation models, GenAI frameworks, agentic AI, and responsible AI practices
• Measure and evaluate AI and GenAI solutions using business, technical, and model quality metrics while continuously optimizing performance

Skills:

• Python, R, SQL, and machine learning frameworks such as TensorFlow or PyTorch
• GenAi applications using Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, vector databases, and model evaluation techniques
• AI frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernal, OpenAI APIs, or equivalent GenAI platforms
• Cloud-based data platforms (GCP, AWS, Azure) and big data technologies
• Statistical modelling and machine learning
• Data management & engineering awareness
• Programming and MLOps practices