Data Scientist

Key Responsibilities

• Lead the development and execution of scalable AI and automation initiatives across the business.
• Identify opportunities to improve operational efficiency, decision-making, and business performance through intelligent, data-driven solutions.
• Design and implement systems leveraging LLMs, RAG, agentic AI frameworks, knowledge graphs, and advanced machine learning techniques.
• Build AI agents and autonomous workflows capable of supporting a 24/7 ecommerce operation.
• Research, evaluate, and implement emerging AI technologies, frameworks, and methodologies.
• Develop, train, validate, and deploy machine learning, NLP, and GenAI models for operational and commercial use cases.
• Apply machine learning techniques including forecasting, optimization, recommendation systems, anomaly detection, and predictive analytics.
• Perform feature engineering, experimentation, model evaluation, benchmarking, and performance optimization.
• Establish evaluation and monitoring frameworks for AI and GenAI model performance.

Data Engineering & Enterprise Analytics

• Build and maintain scalable data pipelines, ingestion systems, and automation workflows.
• Develop scripts and processes for data scraping, collection, cleansing, normalization, and enrichment.
• Integrate and centralize data from APIs, ecommerce platforms, ERP systems, databases, and third-party providers.
• Ensure enterprise data is reliable, accessible, and structured for analytics, reporting, and intelligent automation initiatives.
• Deliver analytics, dashboards, forecasting models, and reporting solutions supporting pricing, finance, merchandising, operations, inventory, and growth initiatives.

Cross-Functional Collaboration

• Partner with stakeholders across Ecommerce, Operations, Finance, Compliance, Legal, Merchandising, Risk, and IT to deliver scalable AI and analytics solutions.
• Translate business challenges into structured AI, machine learning, and data science initiatives.
• Support the adoption and integration of AI-driven tools, workflows, and automation capabilities across the organization.
• Communicate technical findings, insights, and recommendations clearly to both technical and non-technical stakeholders.

Technical Leadership & Best Practices

• Contribute to the design and evolution of scalable AI, data, and analytics architectures.
• Establish best practices for model development, deployment, governance, experimentation, and monitoring.
• Ensure adherence to SDLC standards, documentation, version control, and software engineering best practices.
• Collaborate with Data Engineering and ML Engineering teams to support production deployment and operational scalability.
• Stay current with advancements in AI, machine learning, data engineering, and intelligent automation technologies.