Collaborate with cross-functional teams (Business Engineering Product and Marketing) to define key metrics data requirements and models for analysis experimentation and large-scale datasets to identify trends measure impact and generate insights that improve marketing attribution methodologies and frameworks to assess the effectiveness of campaigns across and build datasets reports and dashboards to monitor campaign and channel performance at ad-hoc analyses to support major initiatives and shape marketing campaign touchpoints and customer journey strategies to deepen predictive models including LTV and propensity models to support campaign optimization and audience the incremental impact of marketing efforts using causal inference and testing how emerging technologies such as generative AI can be applied to create business insights and strategic recommendations to Marketing Business Finance and Executive with teams across Apple to support responsible data collection governance and access.
8 years of experience as a Data Scientist or Analyst in marketing media subscription or tech environments with a proven track record of campaign measurement and optimization.
Strong proficiency in SQL and at least one programming language such as Python or R; experienced in building analytical datasets and pipelines using large-scale structured and unstructured data.
Applied expertise in statistical and experimental techniques including A/B testing regression clustering survival analysis and causal inference.
Demonstrated ability to translate complex data into clear actionable business insights for cross-functional stakeholders.
Bachelors degree in a quantitative field (e.g. Computer Science Statistics Economics) or equivalent professional experience.
Experience in a digital subscription or large-scale e-commerce business.
Deep understanding of marketing effectiveness measurement particularly through causal inference methods.
Masters or Ph.D. in a quantitative field is a plus but not required.
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