Apple · Cupertino

Data Scientist - Marketplace, Apple Ads

(f/m/d) · 4/29/2025

Description

Support Marketplace Product, Engineering, and the Executive Team with analyses, and data products to improve product performance, deepen customer insight and deliver business impact. - Empower the product and algorithm engineering team with insights to advise and fulfill their strategic objectives and goals - Quantify the impact of marketplace initiatives on advertiser impact, and business performance - Design experiments that help define opportunities for pricing, matching, and prediction models leading to improved marketplace performance, and positive advertiser impact - Monitor usage metrics and provide business-based explanations for large scale trends and patterns in advertiser lifecycle behavior - Develop reusable models and data products working closely with Data Engineering team to ensure scalability of models and products in production - Lead business analytics projects through all phases, including defining investigations, exploring data, conducting analysis, interpreting, and presenting results for customers

Qualifications

  • 2+ years of recent experience in a data science role.
  • Programming skills in Python and SQL, and familiarity with machine learning, such as scikit-learn, xgboost, PyTorch, SciPy is necessary.
  • Familiar with statistical analysis methods, most notably regression analysis, causal inference, statistical learning and A/B and quasi-experimentation methods.
  • Demonstrated experience in applying statistics and machine learning to generate clear actionable insights.
  • Comfortable with a variety of data stores such as Hadoop, and Snowflake, familiar with distributed analytics engines such as Spark/PySpark.
  • Possess exceptional communication skills to communicate analyses in a clear and effective manner to technical audience and executive leadership.
  • Demonstrated ability to partner with engineering, meet the data needs of the business, finding creative analytical solutions and develop initial prototypes to address messy business problems.
  • Proven ability to operate comfortably and optimally in a fast-paced and constantly evolving environment.
  • Bachelor's degree in Data Science, Applied Mathematics, Statistics, Computer Science or relevant field/experience

Preferred Qualifications

  • 5+ years of recent experience in a data science role.
  • Experience in the mobile advertising industry or related field.
  • Familiarity with job orchestration frameworks such as Airflow.
  • Demonstrated ability to build visualizations, dashboards and enable broader consumption of insights and tools for investigations.
  • Post-Bachelor's degree in Data Science, Applied Mathematics, Statistics, Computer Science or relevant field/experience

Benefits

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $143,100 and $264,200, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Application

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