At Walmart, we enable the connection between supplier brands and retail shoppers at unprecedented scale. As primary stewards of our brand promise, “Save Money. Live Better,” we work alongside some of the most talented people in the world to engage with the more than 150M households who shop with us. This is a unique opportunity to join a high growth business within the largest company in the world.  We believe all digital advertising can be targeted and accountable – and we have Walmart’s sales data to prove it. Walmart Connect wins when suppliers invest in digital media to drive growth; Walmart and our supplier partners win when your digital expertise helps sell more goods online and offline. Growth in our digital advertising business is key to Walmart’s overall growth strategy. 

About the role: 

The Customer Digital Identity (CDI) organization is enabling the company to better understand who the Walmart customer is. At the same time, we are ensuring streamlined processes, security, scalability, regulatory compliance and best in class customer experience.  The CDI team will amplify customer centricity by deepening the company’s understanding of customers and their preferences. When we do that, we can serve them when and how they need.  This team will work with other teams within the Walmart Membership (W+), Walmart Connect (WMC) and Data Ventures. 

The Senior Data Scientist will lead our development efforts on deploying Machine Learning models in production environment, our other research data scientist experiment. This is more an ml production focus role than machine learning experimentation. The ideal candidate is someone who has good knowledge and background in deployment of machine learning models in production at industrial scale. 

This exciting opportunity will join a team of data scientists embedded within the business unit to develop tools that quantify the impact of investment and operational decisions on the Customer, Associate and Shareholder.  

Location: 

Sunnyvale, CA (preferred), San Bruno, CA 

You’ll sweep us off our feet if… 

• Demonstrable background in ML Model Development involving Classical ML and Deep Learning in various domain.  

• Deep expertise in Machine Learning Development Cycle- development, deployment and Monitoring 

• Strong background of using Model Experimentation (MLFlow), Data Versioning (DVC), maintaining CI/CD pipelines via Jenkins, Github Actions and orchestration pipeline tools (Airflow, Prefect). Similar tools as above is ok.  

• You are skilled in Python. Scala is optional.  

• You are experienced with modeling, optimization, statistical modeling, Ridge/Lasso and elastic net regression. Familiar with ML frameworks: Scikit learn, TensorFlow, PyTorch. 

• You are experienced in ETL practices and basic data engineering. SQL experience is required, working experience in SparkSQL or PySpark is needed. 

• You have experience with cloud environments including Azure, Google Cloud, etc. 

• Solid MLOps practices including good design documentation, unit testing, integration testing and source code control (git). 

• Interest in building capabilities in LLMOps especially around LLM deployment tooling and Evaluation. 

• You are experienced with agile methodologies using project planning and tracking management tools e.g., JIRA 

• You’re naturally curious on how things work and excited by solving challenges at the scale of a Fortune 1 Retailer. 

You’ll make an impact by… 

• Support the development of the Customer Decision & Segmentation Engine. Provide ML Engineer capabilities to cover the entire customer shopping journey. 

• Identify data collection opportunities to better understand the customer/associate experience. Support the build out of data feature store. 

• Help put models into production and mentor other data scientist in doing so. 

Qualifications: 

• Masters degree in Computer Science, Information Technology or related field 

• 5+ years MLOps, Machine Learning in Production experience 

• PhD preferred in Computer Science, Information Technology or related field