Data Scientist focusing on rankings for a luxury fashion marketplace. Collaborating with engineers to enhance user experience and product discovery.
About the role
- Principal Data Scientist designing innovative AI solutions at Walmart Global Tech. Leading projects on AI-powered conversational systems to enhance associate processes and experiences.
Responsibilities
- Collaborate closely with data scientists, machine learning engineers, and software engineers to design, architect, build, deploy, operate, and optimize production-grade AI/ML and GenAI systems
- Design end-to-end architectures for GenAI, agentic AI, and data-intensive applications, ensuring scalability, observability, reliability, security, and responsible AI compliance
- Design and build self-service voice and chat AI systems, including LLM-powered conversational and agentic experiences that support autonomous, multi-step task execution across People Support workflows
- Construct and evolve multi-agent intelligent workflows, translating natural language inputs into goal-directed actions using orchestration frameworks, tools, and robust state and memory management
- Design and develop supporting microservices for AI systems, and integrate them with existing enterprise platforms, APIs, and workflows
- Develop, deploy, and operate production-grade real-time and batch ML/GenAI services, supporting low-latency inference, orchestration, and fault-tolerant execution
- Partner with product managers to design user journeys, feedback loops, and telemetry strategies, and analyze user behavior to continuously improve system and agent outcomes
- Define and own comprehensive evaluation strategies for GenAI and agentic systems, including offline and online evaluation, task success metrics, grounding and hallucination detection, latency and cost controls, A/B testing, and user outcome measurement
- Identify and propose AI/ML and agentic AI use cases that improve business processes, and rapidly build MVPs and POCs to help stakeholders assess feasibility and impact
- Mentor and guide data scientists and ML engineers, helping grow technical depth, system thinking, and business context within the team
- Define and drive responsible AI practices, including safety guardrails, monitoring, governance, explainability, and human-in-the-loop mechanisms to ensure trustworthy AI in production
- Collaborate with applied researchers and platform teams to iteratively improve models, prompts, tools, memory strategies, and MLOps practices
Requirements
- 13+ years of professional experience designing, developing, deploying, and maintaining scalable, production-grade AI/ML and GenAI systems
- Bachelor’s or Master’s degree in computer science, engineering, statistics, mathematics, economics, or a related quantitative field
- Strong industry experience building production AI/ML systems, preferably at large technology companies or AI-native startups
- Extensive experience writing high-quality, production-grade Python code, with strong software engineering fundamentals
- Deep expertise in statistical analysis and machine learning, using frameworks such as TensorFlow, PyTorch, or equivalent
- Hands-on experience building GenAI systems, including Retrieval-Augmented Generation (RAG), prompt engineering, orchestration, and retrieval strategies
- Demonstrated experience developing conversational AI systems, such as chatbots, virtual assistants, or dialogue-driven applications, with a strong understanding of NLP, intent handling, and multi-turn conversation design
- Experience managing conversational state, memory, and context, including session persistence, personalization, and long-lived interactions
- Experience designing and deploying agentic AI systems, including multi-agent workflows, tool use, autonomous task execution, and failure handling
- Demonstrated experience defining evaluation and measurement strategies for GenAI systems, including LLM quality assessment, RAG effectiveness, agent behavior validation, continuous monitoring, and experimentation in production
- Experience building and scaling distributed machine learning systems, including training, inference, and serving
- Familiarity with microservices architectures and enterprise system integration, including API-based communication and collaboration with backend platforms
- Experience with CI/CD pipelines, containerization, and orchestration, including Git and Kubernetes
- Ability to execute and advocate for responsible AI practices with stakeholders across the enterprise
- Strong mentorship and technical leadership skills, with experience guiding engineers and data scientists through complex and ambiguous problems
- Excellent communication skills, with the ability to convey complex technical concepts and insights to both technical and non-technical audiences
- A research-driven, detail-oriented mindset, balanced with a strong bias toward execution and real-world impact
- A collaborative, ownership-oriented approach, with a history of openness, clear communication, and timely decision-making.
Benefits
- maternity and parental leave
- pto
- health benefits
- incentive awards for your performance
- best-in-class benefits
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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