Principal, Data Scientist (Marketplace Risk)

Walmart•Bellevue, WA

4d•Onsite

About The Position

The Principal Data Scientist will lead the development and deployment of advanced machine learning models and solutions to address complex business challenges. The role covers the end-to-end model development lifecycle, from data and feature development through modeling and deployment. The Principal Data Scientist will mentor team members, collaborate across functions, ensure the quality and effectiveness of data sources and models, and apply advanced techniques to drive innovation and measurable business impact while maintaining high standards of integrity and operational excellence. The role is part of Walmart Global Tech’s Marketplace Data Science team, which supports Walmart’s expanding eCommerce platform. The team helps sellers manage digital storefronts, product listings, pricing, and inventory, and develops systems for pre- and post-order experiences. It works with real-time data and partner platforms across Walmart’s ecosystem to deliver customer experiences and scalable, reliable solutions. This is a full-time onsite position based in Bellevue, Washington. Remote and hybrid work are not available. Immigration sponsorship is not available; applicants must be authorized to work for any U.S. employer without current or future visa sponsorship.

Requirements

PhD in Computer Science or a related field and 10 years of relevant data science experience, or a master's degree in a related field and 12 years of relevant data science experience.
Demonstrable experience architecting large-scale machine learning systems.
Deep knowledge and extensive experience with machine learning algorithms, deep learning, and reinforcement learning for developing advanced analytical models.
Experience applying state-of-the-art agentic, generative AI, supervised, and unsupervised machine learning algorithms to real-world problems.
Proficiency in Python and AWS SageMaker for scalable model development and deployment.
Strong knowledge of statistical analysis and exploratory data analysis techniques for interpreting complex datasets.
Ability to translate business requirements into data-driven strategies and measurable outcomes.
Expertise in data source identification, quality assessment, and data ecosystem management.
Leadership skills in mentoring and guiding technical teams.
Effective communication skills for presenting insights and influencing stakeholders.
Must be authorized to work for any employer in the United States without current or future visa sponsorship.

Nice To Haves

Experience in the e-commerce domain.
Experience in the fraud or risk domain.
Demonstrated success on data science platforms such as Kaggle.
Experience with data science, machine learning, or optimization models.
PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, or Econometrics.
Publications or active peer-review experience in related journals or conferences.
Successful completion of one or more assessments in Python, Spark, Scala, or R.
Experience using open-source frameworks such as scikit-learn, TensorFlow, or Torch.
Knowledge of creating inclusive digital experiences, implementing WCAG 2.2 AA standards, assistive technologies, and digital accessibility best practices.

Responsibilities

Develop, test, and deploy advanced machine learning models and data-driven solutions aligned with business requirements.
Analyze complex business problems, identify root causes, and recommend effective technology-focused approaches.
Identify, assess, and validate data sources for quality and relevance to analytical modeling.
Lead feature engineering, exploratory data analysis, and model evaluation to optimize model performance and robustness.
Provide thought leadership and consult with product and business stakeholders to build, scale, and deploy holistic data science products after successful prototyping.
Translate business needs into scalable data science strategies and measurable outcomes in collaboration with stakeholders.
Mentor team members on advanced modeling techniques, data visualization, and best practices.
Integrate emerging machine learning trends and automation tools to drive continuous improvement.
Architect large-scale machine learning systems and guide technical teams toward innovative solutions.