(USA) Senior Manager, Data at San Bruno, California

Senior Manager, Data Science

Bellevue, WA: $132,000 - $264,000/yr San Bruno, CA: $143,000 - $286,000/yr Regular/ Permanent Salaried Position Summary...

About The Walmart Marketplace Decision Management Team Walmart's Decision Management Team supports the growth of the e-Commerce Marketplace program through the practical application of data science and advanced analysis to optimize risk decision strategies. This includes data analysis, advanced statistics, case investigation and application of advanced modeling techniques to manage risk on the ecommerce platform. We work alongside business, product, and engineering teams to deliver solutions to manage Marketplace risk.

What You'll Do... The Senior Manager, DataScience will lead a team of data scientists to define, implement, test, and deploy decision strategies aimed at mitigating fraud and performance risks for Walmart Marketplace. In this role, you will work closely with cross-functional teams, including product, engineering, and data science, to continuously monitor, investigate, and respond to emerging risk trends. You'll be responsible for leveraging advanced data science methodologies to develop and refine risk management models, ensuring the strategies are effective and scalable across both domestic and international portfolios.

How You'll Make an Impact:

• Drive Data Science Innovation to protect the integrity of the Marketplace by applying advanced statistical methods, machine learning, and AI techniques to identify and mitigate fraud and performance risks.
• Support Marketplace Growth by designing and implementing scalable, data-driven risk management solutions that align with key business objectives and growth targets.
• Provide technical leadership and mentorship to your team, overseeing the development of decision models, managing model performance, and ensuring they are optimized for both accuracy and scalability.
• Apply Advanced Data Science Techniques such as predictive modeling, supervised and unsupervised machine learning, deep learning, and anomaly detection to continuously improve risk strategies.
• Collaborate Across Teams to integrate data science models with business processes, ensuring alignment between product, engineering, and data teams to address key risk areas effectively.
• Monitor the performance of deployed models, identify opportunities for improvement, and iterate to enhance their predictive power and robustness in mitigating risks.
• Develop Test & Measurement Frameworks to validate model effectiveness, utilizing rigorous A/B testing, statistical testing, and model evaluation to refine decision strategies.
• Foster Innovation by exploring cutting-edge data science techniques, identifying opportunities to optimize decision-making, and driving improvements in risk management capabilities.

What You'll Bring:

• Deep understanding of machine learning, statistical modeling, and data science techniques used for risk mitigation in e-commerce or marketplace environments.
• Proven ability to build, deploy, and optimize complex data science models to identify and mitigate fraud, performance, and operational risks.
• Proficiency in tools and languages such as Python, R, Spark, Scala, and machine learning frameworks (e.g., TensorFlow, PyTorch, XGBoost) to develop and deploy risk models.
• Ability to understand the end-to-end risk management process, from data ingestion and feature engineering to model deployment and real-time decision making.
• 5-8 years of experience in leading teams or projects related to data science, including mentoring junior data scientists and guiding technical teams toward best practices in model development and deployment.
• Comfortable navigating complex and uncertain situations, making data-driven decisions to improve risk management strategies in a fast-evolving environment.
• Strong ability to translate complex data science concepts into clear, actionable insights for non-technical stakeholders across the organization.
• Understanding how data science and risk management intersect with broader business objectives and the ability to align risk strategies with organizational goals.

Minimum Qualifications:

• Option 1 : Bachelor's degree in Statistics, Computer Science, Data Science, Mathematics, or related field, with 5-8 years of hands-on experience in data science, machine learning, or risk management.
• Option 2 : Master's degree in a related field (e.g., Data Science, Machine Learning, Statistics, Applied Mathematics) with at least 3-5 years of applied experience working on data-driven risk management or fraud prevention.
• Option 3 : 8-10 years of direct experience in data science, machine learning, or applied risk management within an e-commerce or marketplace setting.

Preferred Qualifications:

• Expertise in using advanced machine learning techniques such as deep learning, reinforcement learning, or anomaly detection for fraud detection or risk mitigation.
• Experience with big data technologies like Apache Spark, Hadoop, and cloud-based data solutions (e.g., AWS, Google Cloud) to build scalable risk management platforms.
• Proficiency in data manipulation and analysis tools such as Pandas, NumPy, and SQL for data wrangling, feature engineering, and analysis.

Strong background in model evaluation techniques including ROC/AUC, confusion matrices, precision/recall, and F1 scores, as well as experience with A/B testing and model validation