Subscriptions Marketing Data Scientist, Apple Media Products Data Science - Austin

Austin, TX 78701
  • Job Code
    200281654
  • Jobs Rated
    1st
Summary

Summary

Posted: Aug 24, 2021

Role Number:200281654

At Apple, great ideas have a way of becoming great products, services, and customer experiences very quickly. If you are an ambiti...Summary

Summary

Posted: Aug 24, 2021

Role Number:200281654

At Apple, great ideas have a way of becoming great products, services, and customer experiences very quickly. If you are an ambitious, passionate 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 Subscriptions Marketing Data Scientist, you will help drive growth across Services through attribution, A/B test optimization, and prediction/modeling. This role will involve working with Internet-scale data across numerous customer touch points, undertaking in-depth quantitative analysis, and delivering impactful data products that drive key marketing decisions.

Apple Media Products Data Science encourages independent decision-making and taking smart risks. Our culture is centered around rapid iteration with open feedback and debate along the way. We collaborate 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.

Key Qualifications

  • 3+ years of experience as a Data Scientist or Data Analyst. Preferably for a digital media, MarTech, digital subscription, and/or technology business.
  • Strong proficiency with SQL, Python or R. Experience with data visualization tools such as Tableau a plus.
  • Experience with large scale analytics technologies like Hadoop and Spark preferred. Knowledge of cloud and real-time orchestration technologies such as Kubernetes a plus.
  • Experience in experimentation design, A/B testing, propensity score analysis, linear regression and/or probabilistic modeling.
  • Strong knowledge of statistics, probability distributions, and likelihood estimations for optimization. Applied experience in optimizing test methods through multi-arm bandit and Bayesian approaches a plus.
  • Demonstrated ability to take on projects with a sense of ownership and entrepreneurial mindset
  • Excellent communication, social and presentation skills with meticulous attention to detail

Description

Dive deep into large-scale data to uncover trends and identify key insights that will propel Services marketing strategy.

Craft how best to monitor, measure and understand Subscriptions marketing performance.

Build multi-touch attribution models across various marketing touch points that will serve as benchmarks for optimization.

Partner with stakeholders to optimize key channels and signup funnel efficiency through UX and algorithmic experimentation.

Apply statical rigor to readouts using generally accepted methods of confidence testing and present such insights to partners and executive management.

Collaborate with the ML Data Science team to incorporate production machine learning models into campaigns, customer journeys and signup funnels plus measure their benefits.

Regularly work with data engineering and BI teams to productionalize datasets and automated dashboards to scale channel, campaign and funnel performance monitoring/management.

Strive for new paradigms of marketing optimization that are even more private e.g. through on device aggregation and differential privacy.

Education & Experience

Bachelors degree in Computer Science, Statistics, Mathematics, Engineering, Economics or related field. Ideally, Masters in related field.

Additional Requirements

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Subscriptions Marketing Data Scientist, Apple Media Products Data Science - Austin

Apple, Inc.
Austin, TX 78701

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Data Scientist
1st2019 - Data Scientist
Overall Rating: 1/199
Median Salary: $114,520

Work Environment
Very Good
4/220
Stress
Very Low
42/220
Growth
Very Good
33/220