Senior Human Resources Decision Science Consultant

Disney Disney · Media · Anaheim, CA, USA, Lake Buena Vista, FL, USA

This role focuses on designing, building, and implementing statistical models and machine learning processes to support HR decisions and business processes within Disney Experiences. It involves applying advanced ML and statistical techniques to workforce-related questions, developing scalable data analysis methods, and consulting with business clients to translate needs into analytical solutions. The role requires strong analytical abilities, technical skills in data science programming languages, and the ability to communicate complex findings to non-technical audiences.

What you'd actually do

  1. Design, build, and implement accurate, sustainable, scalable, statistical models and advanced machine learning processes that support HR decisions, business processes, tools, or products.
  2. Apply state-of-the-art machine learning, statistics, or data mining techniques to address key workforce-related questions and measure the causal impact of HR initiatives.
  3. Develop scalable, reproducible, and efficient methods for data analysis and model development.
  4. Build and work with large and complex data sets from multiple systems and outside data sources.
  5. Consult with clients and analysts across the business, translating business objectives and analytical needs into repeatable analytical solutions.

Skills

Required

  • Experience in using statistical, econometric, machine learning, or data mining techniques to inform model development and business decisions in a professional or academic setting.
  • Experience analyzing structured and unstructured data and developing machine-learning models using one or more data science programming languages (e.g., Python or R).
  • Experience building executive-ready presentations and translating complex statistical or predictive modeling results into clear, actionable insights for non-technical audiences.
  • Experience managing multiple analytical projects simultaneously, meeting defined deadlines, and operating effectively with minimal supervision.
  • Experience applying critical thinking and analytical judgment to solve ambiguous problems and rapidly learn or apply new analytical techniques as project needs evolve.
  • Associate or Bachelor degree in Statistics, Economics, Computer Science, Engineering, Mathematics, Analytics / Business Analytics, or other related quantitative field

Nice to have

  • Experience developing dashboards and visualizations using tools such as Tableau and/or Cognos, and querying large-scale database environments (e.g., Snowflake, Redshift).
  • Experience applying supervised and unsupervised machine-learning algorithms and statistical methods, including clustering, PCA, linear and logistic regression, decision trees/random forests, boosted decision trees, forecasting, and model development.
  • Experience designing and executing in analytical workflows using platforms such as Dataiku, Databricks, and/or Alteryx.
  • Experience using Snowflake Cortex to leverage large language models (LLMs) and develop AI-powered applications for analyzing unstructured data.
  • Experience analyzing HR and employee data within enterprise systems such as SAP, SuccessFactors, and/or Workday.
  • Experience applying natural language processing (NLP) methods to extract insights from text data, including classification, topic modeling, sentiment analysis, or entity extraction.
  • Experience operationalizing data science solutions by deploying models into production environments and partnering with engineering or business teams to ensure scalability, reliability, and ongoing performance monitoring.
  • Master in Statistics, Economics, Computer Science, Engineering, Mathematics, Analytics / Business Analytics, or other related quantitative field

What the JD emphasized

  • statistical modeling
  • machine learning
  • data analysis
  • workforce-related issues
  • HR decisions
  • statistical methods
  • machine learning
  • data science programming languages
  • predictive modeling
  • analytical projects
  • analytical techniques
  • machine-learning algorithms
  • statistical methods
  • model development
  • analytical workflows
  • large language models
  • AI-powered applications
  • HR and employee data
  • natural language processing
  • NLP methods
  • text data
  • data science solutions
  • deploying models into production environments
  • quantitative field

Other signals

  • statistical modeling
  • machine learning
  • data analysis
  • workforce-related issues
  • HR decisions