Data Scientist, Marketing Analytics at The Walt Disney Company

New Territories, Hong Kong SAR, Hong Kong SAR

Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Entertainment, Tourism, MarketingIndustries

Master degree in Statistics, Mathematics, Computer Science, Business Analytics, or related disciplines
Proficient in statistical modeling & machine learning techniques, including regression, time series, Bayesian methods, causal inference
Hands-on experience with Python and SQL
8+ years experience in advanced modelling team responsible for solution development and maintenance, from sizable market research agency (senior manager+ grade) or in-house data analytics team
Solid knowledge in crafting and maintaining models for consumer behavior analysis (e.g. predictive modeling, MMM, MTA, driver analysis, ROI model) and simulation
Proficient in Visualization tools e.g. PowerBI, Tableau
Projects experienced shall be covering more on Greater China, across different sectors particularly Tourism, Entertainment
Hands-on experience and project management skill from data crunching, model design and maintenance, data visualization and interpretation
Ability to translate data results using commercial languages is a plus
Excellent verbal communication skills in Cantonese. English is an advantage
Curiosity and detail minded
Innovative but business relevant
Ability to deal with ambiguity
Promote teamwork and relationship building, be able to demonstrate business understanding & engage works according to commercial value & priorities

Own statistical model creation to influence marketing effectiveness, customer engagement, customer satisfaction and commercial performance – to unlock deeper understanding from complex, large-scale datasets
Ideate and develop end-to-end data / statistical models and algorithms for Predictive and Prescriptive Analytics
Incorporate business senses to go beyond a normal data scientist when designing the models, in order to address the business questions from internal stakeholders
Handle big data pool across teams to improve data wrangling efficiency and model readiness