Lead Marketing Experimentation Data Scientist, Apple Media Products Data Science
Santa Clara Valley (Cupertino), California, United States
Software and Services
At Apple, great ideas have a way of becoming great products, services, and customer experiences very quickly. If you are an ambitious, high-energy individual who is not afraid of challenges, we’re looking for you. Apple is seeking an experienced Data Scientist who is passionate about motivating change through the intersection of data and marketing. As a Lead Data Scientist, Experimentation & Testing, you will lead experimentation projects and deliver impactful data products that drive key marketing decisions. This role will support highly visible product marketing, CRM and growth initiatives across several lines of business. We are looking for an individual who can dive deep into the data and also provide thought leadership in various areas of marketing science. The team’s culture is centered around rapid iteration with open feedback and debate along the way. We encourage independent decision-making and taking smart risks. AMP Marketing Data Science & Analytics collaborates with partners across Marketing, Engineering, and Business teams: our mission is to drive innovation at Apple through deep quantitative research of the App Store, Apple Music, Apple TV, and Apple Arcade amongst other services.
- 5+ years of experience in a Data Scientist, Data Analyst, or, Data Engineer. Preferably for a digital media, MarTech, digital subscription business, and/or technology business.
- 2+ years experience in experimentation design, A/B testing, propensity score analysis, linear regression modeling and/or probabilistic modeling.
- 2+ years applied experience in building complex production datasets that enable data science and BI. Experience working with structured and unstructured data stored in distributed files systems such as HDFS and S3.
- Strong proficiency with SQL, Python or R. Experience with data visualization tools such as Tableau as plus!
- We seek a strong background and knowledge in statistics, probability distributions, and likelihood estimations for optimization. Experience in optimizing test methods through multi-arm bandit and Bayesian approaches a plus.
- Integrating production ML models with experiments.
- Experience and/or knowledge in distributed processing frameworks such as Hadoop and Spark. Knowledge of cloud and real-time orchestration such as Kubernetes a plus!
- Curious business mindset with an ability to condense complex concepts and analysis into clear and concise takeaways that drive action
- Demonstrate ability to take on projects with a sense of ownership and ambitious attitude.
- Excellent communication, social and presentation skills with meticulous attention to detail.
Lead the build out of a foundational reporting platform for Marketing experimentation and testing. Work closely with Data Scientists, Engineers, and Product Mangers to implement extensible data products. Work with large petabyte-scale datasets by leveraging distributed computing systems to ingest, data mine, and analyze the data. Define logic for A/B test assignment and experiment measurement using generally accepted methods of confidence testing. Work with Data Engineering teams to enable automation of datasets for a variety of marketing use cases. Take ownership in leading ETL design, feature building, and dataset deployment. Document and QA data pipelines to ensure accuracy. Apply best practices in the area of data science. Partner closely with stakeholders to prioritize and deliver results to specifications and advocate for optimization opportunities. Partner with ML Data Science team incorporate predictive / probabilistic ML models passionate about CRM, retention, and customer journeys. Have a deep understanding of model inputs and outputs. Partner with other Apple organizations on data gathering, data governance, evangelizing key performance indicators and democratizing data. Communicate and present project results and impact effectively. This is a pivotal role that will have visibility at executive levels.
Education & Experience
Minimum of a Bachelors degree in Computer Science, Statistics, Mathematics, Engineering, Econometrics or related field. Ideally, Masters or PhD in related field