Staff, Data Scientist | Sunnyvale

Walmart•Sunnyvale, CA

2d•Onsite

About The Position

The Staff, Data Scientist will use advanced analytics and data science methods to deliver insights supporting strategic business decisions. The role includes identifying and validating data sources, developing predictive models, deploying scalable solutions, and maintaining data quality and governance standards. The successful candidate will work across functions to turn complex business challenges into data-driven solutions using machine learning, statistical analysis, and data visualization. The role also includes mentoring team members and contributing to continuous improvement initiatives. The position is part of Walmart Global Tech’s Global Fraud data science team, which develops real-time and batch decisioning services using machine learning and deep learning to address fraud and risk. The team focuses on a safe, seamless shopping experience, real-time decisioning, an adversarial mindset, and operational excellence through MLOps. This is a full-time, onsite role based in Sunnyvale, California. Remote and hybrid options are not available. Immigration sponsorship is not available; applicants must be authorized to work in the United States for any employer without current or future visa sponsorship.

Requirements

Ph.D. in Computer Science, Data Science, Statistics, Machine Learning, Artificial Intelligence, Mathematics, or a related quantitative field, with 5+ years of relevant industry experience; or a master's degree in a related quantitative discipline, with 7+ years of experience architecting, building, and deploying machine learning solutions in production.
Proficiency in predictive analytics, machine learning algorithms, and advanced statistical methods for developing and validating models.
Strong programming skills in Python.
Experience with data visualization tools such as Matplotlib, Plotly, or Tableau.
In-depth understanding of data strategy, including data quality standards, governance, and scalable data storage solutions.
Ability to translate complex business requirements into data-driven solutions and actionable insights.
Experience with exploratory data analysis, feature engineering, and production model deployment.
Knowledge of SQL and NoSQL database technologies for data sourcing and management.
Excellent communication skills for presenting technical findings to business stakeholders.
Applicants must be authorized to work for any employer in the United States without current or future visa sponsorship.
Minimum qualifications list one of the following: bachelor's degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or a related field and 4 years of analytics-related experience; master's degree in one of those fields and 2 years of analytics-related experience; or 6 years of experience in an analytics or related field.

Nice To Haves

Experience in data science, machine learning, or optimization models.
Ph.D. in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, or Econometrics.
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.
Background creating inclusive digital experiences, including knowledge of WCAG 2.2 AA standards, assistive technologies, and digital accessibility best practices.

Responsibilities

Identify and source relevant data from multiple business domains and distributed data stores for analytics initiatives.
Assess data quality and ensure data is fit for purpose and aligned with business requirements.
Develop and validate predictive models using advanced statistical and machine learning techniques.
Create data visualizations to communicate insights to stakeholders.
Collaborate with cross-functional teams to translate business needs into data-driven solutions and strategies.
Deploy and monitor models in production environments, ensuring scalability and performance.
Mentor team members on analytical methods, coding best practices, and data storytelling.