Expoint – all jobs in one place
מציאת משרת הייטק בחברות הטובות ביותר מעולם לא הייתה קלה יותר
Limitless High-tech career opportunities - Expoint

Apple Marketing Data Scientist - Apple Pay 
United Kingdom, England, London 
217788526

Today
Data Scientist's day-to-day activities will include, but not be limited to: - Perform diagnostic analyses, including ML/statistical modeling and causal inference analyses, to quantify the lift of marketing campaigns. - Analyze marketing campaign performance across channels using sophisticated statistical and econometrics models to derive actionable insights.- Conduct descriptive analysis and deep dives in large-scale data to identify key insights and opportunities that will craft the future of marketing strategy and user experience.- Design and run experiments (A/B, multivariate) to evaluate new product features and marketing strategies, and use statistical methods to ensure valid and reliable results.- Supervise experiment performance, conduct interim analyses, and communicate findings clearly to technical and non-technical audiences.- Build data/model pipelines to automate recurring data pull and modeling processes.
  • Relevant background in Strategy & Planning, Analytics, Data Science
  • Stakeholder Management: Outstanding verbal and written communication skills.
  • Strategic thinking and a consultative approach
  • Programming: Professional experience with SQL and Python.
  • Visualisation: Proven experience with Tableau for data visualisation and dashboard development
  • Prior professional experience in financial products and services
  • Strong experience in Business Analytics and Consulting
  • Proven experience in marketing, advertising, or customer analytics
  • Data handling: Fluency with SQL queries in large and complex fragmented datasets and relational databases.
  • Statistics: Expertise in causal inference modeling and advanced statistical/ econometrics models, including regression and time series modeling.
  • Degree in Analytics, Data Science, Marketing or similar