Data Scientist

Apple • Culver City, CA • 2h ago

Weekly Hours: 40

Role Number: 200645028-0670

Summary

We're idealists. Inventors. Forever tinkering with products and processes, always on the lookout for better. Whether you work at our global offices, offsite, or even at home, a job at Apple will be demanding. But it also rewards forward-thinking, creative thinking and hard work. And none of us here would have it any other way. Does an exciting, dynamic, and fast-paced environment catch your attention? Do you like puzzles and determining solutions that are not obvious? Terrific! Consider joining our team! The Applications team is looking for an outstanding Data Scientist to design, develop, and scale advanced marketing measurement and optimization models. This role will lead the development of Marketing (Media) Mix Models (MMM), budget allocation frameworks across sub-channels, and channel quality prediction models to inform strategic and tactical marketing investment decisions. You will partner closely with Marketing, Business, and Product stakeholders to drive data-informed budget decisions and improve marketing ROI.

Description

The Data Scientist will work on designing, developing, and deploying machine learning-driven marketing measurement and optimization models to inform strategic and tactical marketing investment decisions. The role focuses on building Marketing (Media) Mix Models, budget allocation and optimization frameworks across sub-channels, and predictive machine learning models to assess channel quality and efficiency. This role collaborates closely with Marketing, Product, and Business partners to translate business problems into scalable, explainable, and production-ready ML solutions. The ideal candidate combines strong expertise in machine learning, statistical modeling, causal inference, optimization, and marketing analytics with the ability to communicate insights clearly and drive measurable business impact in a fast-paced, cross-functional environment.

Minimum Qualifications

PhD in Statistics, Mathematics, Economics, Operations Research, or a related quantitative field with 3+ years of relevant experience; or MS with 5+ years of experience in applied modeling and optimization roles.

Strong hands-on experience with Marketing / Media Mix Models (MMM), including budget allocation, optimization frameworks, marginal ROI analysis, and incorporating experimentation results (e.g., geo-lift tests, A/B experiments).

Experience modeling marketing performance at granular levels, including sub-channel dimensions such as creative, placement, audience, and brand vs. performance.

Strong foundation in statistics, regression, time-series analysis, causal inference, and optimization, with familiarity in Bayesian MMM, hierarchical models, or probabilistic forecasting.

Experience designing, implementing, and evaluating ML models in Python, with solid understanding of model validation techniques and performance metrics.

Proven ability to work with large-scale, multi-source datasets, and apply quantitative methods to real-world business problems.

Working proficiency with distributed data systems and big data tools (e.g., Hadoop, Spark).

Strong communication, collaboration, and stakeholder influence skills, with a demonstrated track record of driving business impact through data-driven recommendations.

Preferred Qualifications

Ability to translate research ideas into scalable, production-level ML/AI solutions.

Experience in building and maintaining machine learning pipelines, including data preprocessing, model training, and deployment.

Experience developing AI tools, frameworks, or APIs to support model deployment or LLM-based applications.

Familiarity with generative AI techniques (e.g., diffusion models, transformer architectures)

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote 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) .

Apple

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