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About This Role
Marketing Science is an applied science team within The Walt Disney Company Marketing organization. The team is interdisciplinary and cross-functional. Members of the team draw on knowledge and expertise from a wide range of academic disciplines, but primarily in areas related to applied mathematics and the social sciences, in order to advance data-driven marketing at The Walt Disney Company. Depending on their backgrounds and interests, they may engage in market research, experimentation, forecasting, causal measurement, optimization, or data-related automation and data product development. Marketing Science is a part of Performance Marketing at The Walt Disney Company and is closely integrated with Performance Marketing operations. Members of the Marketing Science team generally learn about the advertising and marketing technologies that connect these operations in order to find opportunities to improve marketing effectiveness.
We are seeking an Associate Marketing Data Scientist to join the Marketing Science team at Disney. Members of the Marketing Science team generally fall within one or more functional areas as described in the Specializations section below. A person in this role is an individual contributor who is developing expertise in one or more functional area and has attained significant capabilities so as to be able to help complete tasks within those functional areas with supervision. You are expected to be developing knowledge in media, marketing, and entertainment.
The role reports to a VP, Marketing Science and is based in Celebration, FL
You Will / Responsibilities:
As an Associate Marketing Data Scientist you will work within the following specializations:
Research and Consumer Insights Specialization:
- Overseeing or carrying out all aspects of quality market research, including overseeing proper sampling, assigning sample weights, participating in survey / research design, and validating survey programming.
- Designing innovative research studies that uncover not only consumer trends and behavior but also the beliefs, values and motivations of consumers
- Using discrete choice models and other advanced analytic techniques to better understand the way consumers make decisions
- Using unsupervised learning models and other advanced analytic techniques to produce and evaluate data-driven customer segmentations for various marketing purposes
- Synthesizing audience and consumer insights into compelling narratives for business stakeholders in The Walt Disney Company Marketing organization
- Overseeing consumer research collected as part of brand lift studies
- Coordinating work with other research teams at The Walt Disney Company
Marketing Measurement and Valuation Specialization:
- Troubleshooting problems with existing media measurement solutions, including problems that arise from changes in the martech or adtech measurement ecosystem (e.g. iOS14 update, cookie deprecation, etc.)
- Consulting with business stakeholders within TWDC media team (or adjacent teams within TWDC Marketing) to design business processes or campaign architectures that achieve better results on various media platforms including DSPs (DV360, The Trade Desk, Amazon DSP, etc.) or walled gardens (YouTube, Facebook, Twitter, TikTok, etc.)
- Consulting with business stakeholders within TWDC media team (or adjacent teams within TWDC Marketing) to precisely formulate media business problems that might benefit from data-driven solutions
- Using knowledge of media measurement—including, but not limited to knowledge of pixels, SDKs, server-to-server integrations, media platform APIs, and identity graphs—to design measurement solutions for media business problems
- Translating media business problems into formal problems that can be solved by technical teams
- Validating whether proposed solutions and existing products successfully address media business problems
- Overseeing design, execution, and analysis of A/B tests in order to evaluate the effectiveness of media / marketing exposure across different dimensions
- Designing, interpreting or otherwise using formal models (e.g. marketing mix models, attribution models) to measure causal effects of marketing or media
- Building financial valuation models of marketing or media performance in order to demonstrate the value created by application of scientific approaches or uncover additional areas for improvement
- Using concepts and principles of economics to better understand the effects of competition on marketing
- Using concepts and principles of economics or finance to better understand the effects of marketing on the lifetime value of movies
Statistics, Data Science, and Optimization Specialization
- Using knowledge of statistics, machine learning, and AI to solve technical and research problems, including problems related to experimental and research design
- Developing and interpreting the results of new formal models to forecast or predict business outcomes
- Developing and interpreting the results of new formal models (e.g; marketing mix models, attribution models, etc) to measure causal effects of marketing efforts or paid media
- Overseeing the maintenance and improvement of existing statistical and machine learning models
- Prototyping data pipelines or tools to automate the creation and/or deployment of knowledge gained from statistical models, machine learning models, or artificial intelligence
- Formulating and solving optimization problems
- Applying understanding of concepts and principles of optimization to evaluate and improve media and marketing performance
- Formulating and solving formal optimization problems
- Effectively leveraging big data and cloud computing technologies to address business questions
AI, ML, Analytics, and Data Engineering and Technology Specialization
- Architecting, designing, and building data products using foundational data sets that are regularly refreshed by data engineering teams
- Developing and maintaining code for data products
- Consulting regularly with business stakeholders and data product owners or managers on current data assets and data strategy in order to ensure that the right data is acquired, maintained, and used properly to solve recurring media and marketing problems within the entertainment and media industries
- Leveraging business knowledge from business stakeholders to provide specifications and requirements to technology and data engineering teams for ingesting, transforming, and cleaning foundational data sets that can be leveraged downstream for building data products
- Providing documentation and instruction to other scientists, engineers, or analysts about how to use data products to automate and scale data-retrieval, generation of data results, data-driven recommendation, or data-driven decision-making
- Leveraging business knowledge from business stakeholders to integrate and transform data from various foundational data sets as required to create successful data products
- Using code and technology to build data pipelines or tools to automate the creation and/or deployment of knowledge gained from statistical models, machine learning models, or artificial intelligence
- Monitoring and overseeing the deployment, maintenance, and improvement of the statistical and machine learning models and optimization routines embedded in data products
- Building machine learning models, statistical models, or optimization routines—especially for the purposes of automating and scaling tasks—while leveraging the expertise of others to ensure that best data and decision science practices are followed
- Implementing bespoke, rigorous methodologies in order to systematically solve marketing problems in repeatable ways
- Working directly with business stakeholders, analysts, and researchers to develop code to implement bespoke, rigorous analytic or business processes in order to systematically provide insights or recommendations in repeatable ways
- In concert with the Marketing Technology team, designing and building GenAI‑powered data products that transform raw, multi‑modal data into actionable insights, predictions, and user‑facing experiences
- Architecting end‑to‑end AI systems that combine classical ML, deep learning, vector search, retrieval‑augmented generation (RAG), and other techniques to deliver reliable, scalable intelligence
- Translating ambiguous product and business problems into AI‑first solutions, defining data requirements, modeling approaches, and success metrics
- Building and maintaining data pipelines and feature stores that support real‑time and batch inference for GenAI and ML workloads
- Implementing evaluation, monitoring, and observability frameworks for GenAI systems, including hallucination detection, bias analysis, latency/cost optimization, and model drift
- Coordinating work with other science and technology teams at The Walt Disney Company
You Will Have / Required Qualifications:
- Desire to learn about media and marketing and the overall entertainment / media industries
- Demonstrated experience with organizing, prioritizing, and balancing concurrent projects and sustainment activities
- Demonstrated experience with acquiring, organizing, cleansing, manipulating, and merging large volumes of data
- Demonstrated experience with performing exploratory quantitative analysis
- Demonstrated experience with fitting, evaluating, and interpreting relevant statistical or machine learning models
- Demonstrated experience with Python
- Familiarity with SQL, using GenAI-powered tools for coding, Tableau or other data visualization tools, cloud computing services and CI/CD and GitLab or GitHub
- Desire to learn about the martech and adtech ecosystem
- Strong business acumen in addition to strong technical skills
- Demonstrated potential for developing strong creative problem-solving, decision-making and critical thinking skills
- Demonstrated strong interpersonal skills, including ability to partner with others to handle multiple priorities in a rapidly changing environment
- Demonstrated strong written and verbal communication skills
- Ability to work independently as well as across teams in a diverse, international, collaborative environment
Preferred Qualifications:
- Proficiency with Python and experience with SQL
- Demonstrated experience using GenAI-powered tools for coding
- Demonstrated experience with Tableau or other data visualization tools
- Demonstrated experience using big data technologies for data science—particularly Spark
- Proficiency in acquiring, organizing, cleansing, manipulating, and merging large volumes of data
Required Education:
- Bachelor's degree with significant academic exposure to formal or quantitative methods + Demonstrated academic achievement in statistics and probability—typically involving graduate-level coursework + Further demonstrated academic achievement in areas related to specialization:
- Research and Consumer Insights: relevant areas include (but are not limited to) psychology, sociology, and marketing
- Marketing Measurement and Valuation Specialization: relevant areas include (but are not limited to) cognitive science, economics, applied statistics, and business
- Statistics, Data Science, and Optimization: relevant areas include (but are not limited to) statistics, data science, applied mathematics, and operations research
- AI, ML, Analytics, and Data Engineering and Technology: relevant areas include (but are not limited to) computer science and applied mathematics.
Preferred Education:
- Graduate degree (Master’s or Doctorate)—with significant graduate-level academic exposure to formal or quantitative methods
Additional Information:
Disney offers a rewards package to help you live your best life. This includes health and savings benefits, educational opportunities and special extras that only Disney can provide. Learn more about our benefits and perks at https://jobs.disneycareers.com/benefits
The hiring range for this position in Celebration, FL is $92,900.00 to $124,500.00 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.
Salary Context
This $92K-$124K range is in the lower quartile for Data Scientist roles in our dataset (median: $161K across 138 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At The Walt Disney Company, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,503 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $111,000. This role's midpoint ($108K) sits 43% below the category median. Disclosed range: $92K to $124K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
The Walt Disney Company AI Hiring
The Walt Disney Company has 1 open AI role right now. They're hiring across Data Scientist. Based in Celebration, FL, US. Compensation range: $124K - $124K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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