Solution Architect, Data Science - Retail

Are you passionate about designing scalable, data-driven solutions for some of the world’s most dynamic retail organizations? We’re looking for a Solution Architect to join NVIDIA’s EMEA Retail Team, focusing on supply chain data science: forecasting, business analytics, and operations optimization. In this role, you’ll help leading Retail and CPG companies AI platforms that bring predictive intelligence and automation to every layer—from real-time demand planning and logistics optimization to customer recommendation tools.

You’ll combine your deep knowledge of data systems, analytics frameworks, and machine learning pipelines with NVIDIA’s industry-leading GPU and AI platforms to drive measurable business impact across global retail ecosystems. We need a passionate, enthusiastic, and creative individual to help us pursue major opportunities in the retail industry with specific focus on NVIDIA's Enterprise Platforms, GPUs, networking, and software stacks with Open Source platform solutions.

What You’ll Be Doing:

• You will be part of our EMEA Retail Solution Architecture Team working to drive NVIDIA technology adoption at key Retail & CPG customers and secure design wins in both Data Center, Edge and Cloud Deployments.

• Becoming a trusted technical advisor with our customers, work on exciting projects and deploy AI solutions in production using Data Processing for Retail.

• Leading customer proof-of-concepts (PoCs) of next-gen platforms for deploying Retail Industry solutions to key use-cases like forecasting, logistics, and customer recommendation and personalization, real-time and at scale.

• Collaborating with NVIDIA Engineering, Product, Sales teams on innovative AI optimization techniques, operating at the intersection of algorithms and systems, for assistance in developing solutions and providing customer feedback to enable development and growth of our products.

• Driving adoption of NVIDIA's Accelerated Compute Platforms with focus on growing our customer's Software, DevOps and MLOps capabilities by using NVIDIA's modern architectures, solutions and blueprints to deploy leading computing platforms.

What We Need to See:

• MS or PhD in Computer Science, Engineering, Mathematics, or related technical field (or equivalent experience).

• 5+ years of experience in data architecture, data science solutions, or large-scale analytics systems, working with enterprise developers and academic research community,

• Demonstrate detailed industry knowledge of Retail Analytics (demand forecasting, supply chain optimization, personalized recommendations, …)

• Experience with ML frameworks or data science stacks (PyTorch, TensorFlow, RAPIDS, etc.) and deep understanding of data pipelines, ETL processing, and real-time data streaming, parallel or distributed frameworks (e.g., Spark, Kafka, Databricks, Snowflake),

• Enjoy collaborating with teams across the organization such as Engineering/Research, Sales, Product, and Marketing, able to think creatively to debug and solve complex problems

• Excellent communication and presentation skills with the ability to drive technical discussions with both engineers and executives.

Ways to Stand Out from the Crowd:

• Expertise in supply chain optimization, recommender systems, or behavioral data analytics at large scale,

• Strong fundamentals in optimization and decision intelligence (e.g., linear/ mixed-integer programming, routing, allocation, or planning algorithms), · Experience with NVIDIA GPUs and software libraries, such as CuOpt, cuVS, RAPIDS, NeMo Retriever or NVIDIA NeMo Framework,

• Prior experience with AI at scale on cloud environments (e.g., AWS, Azure, GCP) and on-premises infrastructure

• Experience with containerization and orchestration technologies, monitoring, and observability solutions for AI deployments

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and dedicated people in the world working for us. If you're creative and autonomous, we want to hear from you.