Note: The job is a remote job and is open to candidates in USA. Galls is an ecommerce-driven business seeking a highly technical and commercially minded AI/ML Data Scientist. This role involves building intelligent agents, automation systems, and scalable workflows to enhance operational efficiency and support business growth initiatives.
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
- 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
- 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
- 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
- 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
Skills
- Hands-on AI/ML model development
- Data engineering
- AI architecture
- Analytics
- Strategic business problem-solving
- Design and implement intelligent systems that improve operational efficiency
- Automate workflows
- Optimize pricing and merchandising
- Enhance financial analysis
- Support business growth initiatives
- Work closely with cross-functional teams across Ecommerce B2C/B2B cycles
- Build practical AI solutions leveraging modern machine learning, LLMs, Retrieval Augmented Generation (RAG), agentic AI systems, and advanced analytics frameworks
- Take ownership of enterprise AI initiatives
- Build scalable systems that directly impact business performance
- 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
- 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
- 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
- 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
- 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
- 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
- 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
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