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Apple ML Data Scientist - Forecasting (Consumer Analytics), Ad Platforms in Austin, Texas

ML Data Scientist - Forecasting (Consumer Analytics), Ad Platforms

Austin,Texas,United States

Machine Learning and AI

At Apple, we believe in the power of technology to enrich people's lives. Everything we build is designed to empower people, including our advertising platform. We deliver ads in a way that benefits both consumers and advertisers — helping people discover content, supporting creators, and protecting and respecting everyone’s privacy. Our technology makes advertising possible on the App Store, Apple News, Stocks, and Apple TV. We help developers and marketers of all sizes drive app discovery across the App Store. Our display ads on Apple News and Stocks let advertisers promote their products alongside trusted content in a brand-safe environment, while supporting publishers and journalists. Sponsorship integrations and experiences in live sports on Apple TV help advertisers connect with captivated audiences. Everything we do is with the unwavering commitment to privacy you expect from Apple. Because when advertising is done right, it benefits everyone.

Description

We're seeking a Machine Learning Data Scientist to join our Consumer Analytics Team. As a key member of our data-centric team, you'll drive the exploration, analysis, development, execution, and measurement of analytical solutions critical to our business. Your work will transform data generated by user searches, app content, and App Store context into business insights, improving customer experiences and driving discovery and productivity for app developers! Successful analytics teams include data scientists and data engineers working hand in hand to build insightful and efficient solutions. In this role, you'll be a key player in a multi-functional team that delivers insights with direct and measurable impact! - Support Finance, Product, Sales, and the Executive Team with forward looking expectations of our marketplace performance. - Design and evaluate strategies that help define opportunities for increased usage, improved marketplace performance, and greater customer happiness - Monitor usage metrics and provide business-based explanations for large scale trends and patterns in advertiser lifecycle behavior. Detect and surface anomalies - Develop reusable models and assets working closely with Data Technology team, to ensure scalability and industrialization as models move into production - Lead business analytics projects through all phases, including defining investigations, exploring data, conducting analysis, interpreting, and presenting results

Minimum Qualifications

  • 6+ years of recent experience in data science or machine learning role

  • Working experience with Python and SQL

  • Comfortable with hybrid cloud technologies such as Hadoop, AWS, Snowflake, Spark and PySpark

  • Experience in quantitative analysis including time-series, regression, classification, and clustering

  • Ability to clearly and effectively communicate the results of analyses with product and leadership teams, to influence the overall strategy of the product

  • Ability to partner with engineering, meet the data needs of the business, find creative analytical solutions and develop initial prototypes to address messy business problems

  • Experience with end-to-end implementation of a model prototype specifically training, processing, feature engineering, evaluating model outputs, and putting models into production

  • Ability to operate comfortably and effectively in a fast-paced, multi-functional, continuously evolving environment

  • Bachelor's in Computer Science, Applied Mathematics or similar quantitative field

Key Qualifications

Preferred Qualifications

  • 6+ years of recent experience in data science or machine learning role, within digital advertising industry or related field

  • Familiarity with packages such as scikit-learn, XGBoost, PyTorch, and JAX is highly desirable

  • Deep learning and Time series forecasting is highly desired.

  • Familiarity with job orchestration frameworks like Airflow

  • Graduate degree in Computer Science, Applied Mathematics or similar quantitative field

Education & Experience

Additional Requirements

  • Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics.Learn more about your EEO rights as an applicant. (https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf)

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Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant (Opens in a new window) .

Apple will not discriminate or retaliate against applicants who inquire about, disclose, or discuss their compensation or that of other applicants. United States Department of Labor. Learn more (Opens in a new window) .

Apple will consider for employment all qualified applicants with criminal histories in a manner consistent with applicable law. If you’re applying for a position in San Francisco, review the San Francisco Fair Chance Ordinance guidelines (opens in a new window) applicable in your area.

Apple participates in the E-Verify program in certain locations as required by law. Learn more about the E-Verify program (Opens in a new window) .

Apple is committed to working with and providing reasonable accommodation to applicants with physical and mental disabilities. Reasonable Accommodation and Drug Free Workplace policy Learn more (Opens in a new window) .

Apple is a drug-free workplace. Reasonable Accommodation and Drug Free Workplace policy Learn more (Opens in a new window) .

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