AI/ML Data Scientist

AI/ML Data Scientist

Position Overview

As an ecommerce-driven business operating in a fast-moving, always-on environment, we are seeking a highly technical and commercially minded AI/ML Data Scientist to build intelligent agents, automation systems, and scalable workflows that can continuously monitor, analyze, and execute business processes at scale. The objective is to develop intelligent, data-driven capabilities that enable the business to operate more efficiently, proactively, and intelligently 24/7.

This role combines hands-on AI/ML model development, data engineering, AI architecture, analytics, and strategic business problem-solving. The ideal candidate will design and implement intelligent systems that improve operational efficiency, automate workflows, optimize pricing and merchandising, enhance financial analysis, and support business growth initiatives.

You will work closely with cross-functional teams across Ecommerce B2C/B2B cycles to build practical AI solutions leveraging modern machine learning, LLMs, Retrieval Augmented Generation (RAG), agentic AI systems, and advanced analytics frameworks.

This role offers the opportunity to take ownership of enterprise AI initiatives while building scalable systems that directly impact business performance.

Key Responsibilities

AI Strategy & Intelligent Automation

• 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

AI/ML Modeling & Solution Development

• 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
• Support the continuous improvement, scalability, and reliability of AI/ML systems and analytics solutions
• 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

Preferred Qualifications

• Master’s or PhD degree in Computer Science, Machine Learning, Engineering, or another highly quantitative discipline
• 5+ years of hands-on experience building AI/ML/NLP solutions and applying statistical analysis to solve complex business problems
• Strong programming skills in Python and SQL, with experience developing scalable production-ready solutions
• Experience designing and deploying systems leveraging LLMs, RAG pipelines, agentic AI frameworks, vector databases, and semantic search
• Experience with modern AI/ML frameworks and tooling such as LangChain, LangGraph, CrewAI, OpenAI SDK, Hugging Face, PyTorch, and related ecosystems
• Experience working with data pipelines, APIs, ETL workflows, and cloud-based data platforms
• Familiarity with vector stores, graph databases, SPARQL, Linux environments, and modern software engineering practices
• Experience with experimentation, benchmarking, model evaluation, and LLM performance assessment methodologies
• Strong understanding of SDLC principles, Git/version control workflows, and scalable software architecture
• Experience supporting ecommerce, B2B/B2C operations, merchandising, pricing, finance, fraud/risk, or operational analytics initiatives
• Strong analytical, communication, problem-solving, and stakeholder management skills

EOE/Disability/Veterans