Sr Analyst-Advanced Analytics & BI

The Walt Disney Company•Celebration, FL

13h•Hybrid

About The Position

The Media Growth and Marketing team for Disney Experiences (DX), powers campaign activation. We lead the strategy and planning of audience-focused, data-driven communications across the offline (TV, radio, out-of-home), digital (display, social, search and streaming) and direct channels (email, direct mail and app push). We're seeking a Sr. Analyst to drive advanced analytics for Direct Channel marketing. Using machine learning and business intelligence, you will develop data-driven insights and products to optimize audience targeting, messaging, and channels. You’ll collaborate with other planning and marketing teams to deliver actionable insights and recommend solutions. This roles sit in Celebration, FL and reports to the Sr Manager – Advanced Analytics & Business Intelligence (BI)

Requirements

3-5+ years of experience in customer-focused analytics within industries such as hospitality, entertainment, or retail, including sales, marketing, and customer service functions
3-5+ years of hands-on expertise in BI technologies, data visualization, A/B testing, and the application of machine learning algorithms such as linear regression, logistic regression, decision trees, and clustering
Strong understanding of relational databases, data pipelines, and exploratory data analysis (EDA) using SQL and Python with large-scale datasets
Experience with storytelling and presenting insights from quantitative information for non-technical stakeholders
Proven ability to translate business questions into actionable analytical products
Time-management skills, with ability to prioritize multiple requests and communicate status to partners/leadership
Critical thinking, attention to detail, and a curiosity-driven mindset.
Machine Learning, SQL, Python or R

Nice To Haves

Snowflake
Dataiku
Tableau
Master’s degree in Statistics, Analytics, Business, Mathematics, Computer Science, Marketing, Psychology, or equivalent work experience

Responsibilities

Translate business and analytic needs into data requirements to build data pipelines for predictive models, actionable insights and operational dashboards.
Compile and cleanse data across a variety of domains in a large, complex data environment to create a foundation for analytic work.
Leverage data science skills to deliver predictive models, ad hoc statistical analyses and data visualizations.
Conduct end-to-end analysis including data collection and exploration, feature generation and reduction, and algorithm selection.
Determine the best techniques to use to solve problems efficiently and effectively, examining all aspects of the analytics curve across statistical, predictive, and prescriptive methods.
Design, develop, and deploy machine learning models (e.g., logistic regression, classification, decision trees, XGBoost, LightGBM, clustering) to predict guest behavior including purchase likelihood, and engagement.
Develop intuitive, impactful dashboards and visual analytics using tools (e.g., Tableau) to enable self-service insights, monitor campaign metrics, and support data-driven decision-making across stakeholders.
Design experiments (A/B testing) to measure audience strategy effectiveness.
Interpret outputs of analyses and models to make relevant recommendations.
Draw inferences, analyze KPIs, guest behavior trends and identify limitations to accurately summarize findings, relating them back to the problem at hand and drive business impact.
Craft key takeaways and next steps in the context of business actionability, while facilitating the integration of work into the strategic decision making process.
Mentor and lead data analytics best practices by promoting code quality, model validation, reproducibility, and responsible AI practices including performance monitoring, bias mitigation, and stakeholder education.
Productionalize data, models and dashboards to make them available for marketing operations.
Architect and deploy department data assets, including data pipelines, predictive models and data visualizations, using Python, SQL and cloud platforms (e.g. Snowflake, Dataiku).