Principal, Data Scientist (Marketplace Risk)

Walmart•Bellevue, WA

2d•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 spans the end-to-end model development lifecycle, from data and features through modeling and deployment. The Principal Data Scientist will mentor team members, collaborate cross-functionally, ensure the quality and effectiveness of data sources and models, and drive innovation through advanced techniques and measurable business impact while maintaining integrity and operational excellence. The Marketplace Data Science team at Walmart Global Tech supports a rapidly expanding eCommerce platform. It helps sellers manage digital storefronts, product listings, pricing, and inventory, and enables seamless pre- and post-order experiences through intelligent, resilient systems. The team uses real-time data and partner-platform integrations across Walmart’s ecosystem, building scalable and reliable solutions in collaboration with other functions. This is a full-time onsite role based in Bellevue, Washington. Remote and hybrid options are not available. Immigration sponsorship is not available; applicants must be authorized to work for any employer in the United States 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, as stated in the role summary.
Demonstrable experience architecting large-scale machine learning systems.
Deep knowledge of and extensive experience with machine learning algorithms, deep learning, and reinforcement learning for developing advanced analytical models.
Proven experience applying state-of-the-art agentic, generative AI, supervised, and unsupervised machine learning algorithms to real-world problems; eCommerce experience is preferred.
Proficiency in Python programming and AWS SageMaker for scalable model development and deployment.
Strong knowledge of statistical analysis and exploratory data analysis techniques for interpreting complex data sets.
Ability to translate business requirements into data-driven strategies and measurable outcomes.
Expertise in data source identification, quality assessment, and data ecosystem management.
Proven leadership skills in mentoring and guiding technical teams toward innovative solutions.
Effective communication skills to present insights and influence stakeholders.
Applicants must be authorized to work for any employer in the United States without current or future visa sponsorship.
The posting’s minimum qualifications list one of the following: a bachelor’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or a related field and 5 years of experience in an analytics-related field; a master’s degree in one of those fields and 3 years of experience in an analytics-related field; or 7 years of experience in an analytics or related field.

Nice To Haves

Experience in the e-commerce domain.
Experience in the fraud or risk domain.
Demonstrated success in data science platforms such as Kaggle.
Preferred qualifications include data science, machine learning, optimization models, a PhD in a related quantitative or technical field, publications or active peer review in related journals or conferences, successful completion of one or more assessments in Python, Spark, Scala, or R, and experience using open-source frameworks such as scikit-learn, TensorFlow, or Torch.
Knowledge of inclusive digital experiences, 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 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.
Consult with product and business stakeholders to build, scale, and deploy holistic data science products following 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.