Job Title:  Associate Marketing Scientist

Marketing Science is an applied science team within The Walt Disney Studios 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 Studios.  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 The Walt Disney Studios Performance Marketing 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.

Job Responsibilities: 

Members of the Marketing Science team generally fall within one or more functional areas as described in the Analytics Specializations section below.  A person in the Associate Marketing (Data) Scientist 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.  A person in the Associate Marketing (Data) Scientist role is expected to be developing knowledge in media, marketing, and entertainment.

Analytics Specializations:

Research and Consumer Insights Specialization (more emphasis in some Marketing Science roles):

• 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 Studios 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 (more emphasis in some Marketing Science roles):

• 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 TWDS media team (or adjacent teams within TWDS 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 TWDS media team (or adjacent teams within TWDS 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 (more emphasis in Marketing Data Science roles)

• 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
• Coordinating work with other science and technology teams at The Walt Disney Company
• 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

Analytics Engineering and Technology Specialization (more emphasis in Analytics Engineering roles, not as relevant for Associate level)

• 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 data or decision scientists as appropriate and necessary to ensure that best data and decision science practices are followed
• Working directly with data scientists and decision scientists to implement 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
• Coordinating work with other science and technology teams at The Walt Disney Company

Basic Education

• BS or MS in advanced Mathematics, Statistics, Data Science and comparable field of study and/or equivalent work experience
• 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 
  • Analytics Engineering and Technology: relevant areas include (but are not limited to) computer science and applied mathematics.

Basic Qualifications

• Desire to learn about media and marketing
• Desire to learn about the entertainment / media industries
• Some 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
• 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
• Familiarity with Tableau or other data visualization tool
• Familiarity with cloud computing services
• Familiarity with CI/CD and GitLab or GitHub
• Desire to learn about the martech and adtech ecosystem

Preferred Education

• Graduate degree (Master’s or Doctorate)—with significant graduate-level academic exposure to formal or quantitative methods

Preferred Qualifications

• Proficiency with Python
• Demonstrated experience with SQL
• Demonstrated experience with Tableau or other data visualization tool
• Demonstrated experience using big data technologies for data science—particularly Spark
• Proficiency in acquiring, organizing, cleansing, manipulating, and merging large volumes of data

The hiring range for this position in CA is $93,400.00 to $125,200.00 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.