STAFF, DATA SCIENTIST (MLE)

Position Summary

This is the team which builds reusable technologies that aid in acquiring customers, onboarding and empowering merchants besides ensuring a seamless experience for both these stakeholders. We also optimize tariffs and assortment, adhering to the Walmart philosophy - Everyday Low Cost. In addition to ushering in affordability, we also create personalized experiences for customers the omnichannel way, across all channels - in-store, on the mobile app and websites.

What You Will Do

• Staff Machine Learning Engineer is responsible for building scalable end-to-end data science solutions for our data products.
• Work closely with data engineers and data analysts to help build ML- and statistics-driven data quality and continuous data monitoring workflows
• Solve business problems by scaling advanced Machine Learning algorithms and complex statistical models on large volumes of data
• Own the MLOps lifecycle, from data monitoring to refactoring data science code to building robust model monitoring workflows for model lifecycle management
• Demonstrate strong thought-leadership and consult with product and business stakeholders to build, scale and deploy holistic machine learning solutions after successful prototyping.
• Follow industry best practices, stay up to date with and extend the state of the art in machine learning research and practice and drive innovation
• Promote and support company policies, procedures, mission, values, and standards of ethics and integrity.

What You Will Bring

• Knowledge of the foundations of machine learning and statistics
• Experience with web service standards and related patterns (REST, gRPC)
• Experienced in architecting solutions with Continuous Integration and Continuous Delivery in mind
• Familiar with distributed in-memory computing technologies

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.