(usa) Staff, Data Scientist

Walmart Walmart · Retail · SUNNYVALE TECH CORNERS BLDG 6 CA SUNNYVALE

Staff Data Scientist role focused on developing and deploying scalable machine learning models for fraud and risk detection in e-commerce and financial services. The role involves data exploration, feature engineering, model development using advanced ML/DL techniques, and partnering with cross-functional teams for production deployment.

What you'd actually do

  1. Perform hands-on data exploration, processing, and analysis on large-scale datasets.
  2. Apply machine learning, deep learning, and data mining techniques to develop robust predictive models for eCommerce fraud detection, financial services fraud detection, anomaly detection, and abusive returns.
  3. Extract and analyze data from data warehouses and cloud platforms; perform feature engineering, back-test models, evaluate model performance, and present findings and recommendations to stakeholders.
  4. Research, develop, and implement state-of-the-art algorithms that address complex business challenges.
  5. Partner with cross-functional teams to deploy scalable, production-ready data science solutions.

Skills

Required

  • data mining
  • machine learning
  • statistical analysis
  • deep learning
  • Python
  • open-source machine learning and data science frameworks
  • large datasets
  • distributed computing platforms (BigQuery, Hive, or Spark)
  • cloud environments (GCP or Azure)
  • database and querying skills (BigQuery, Hive, and SQL)
  • Python, Java, or JavaScript programming
  • Advanced degree in a STEM field (Master's or PhD preferred) and at least five years of relevant industry experience

Nice to have

  • AI agents
  • practical AI-driven applications
  • Data science
  • machine learning
  • optimization models
  • PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics
  • Successful completion of one or more assessments in Python, Spark, Scala, or R
  • Using open source frameworks

What the JD emphasized

  • Fraud and Risk Detection Data Science team
  • eCommerce fraud detection
  • financial services fraud detection
  • anomaly detection
  • abusive returns
  • scalable predictive models
  • scalable solutions
  • production-ready data science solutions
  • state-of-the-art algorithms

Other signals

  • develop scalable predictive models
  • deploy scalable, production-ready data science solutions
  • Fraud and Risk Detection Data Science team
  • large volumes of business and customer data
  • eCommerce fraud detection, financial services fraud detection, anomaly detection, and abusive returns