Machine Learning Data Scientist, Productivity AppsAt Apple, new ideas have a way of becoming phenomenal products, services, and customer experiences very quickly! The Productivity Apps teamthe team behind apps like Notes, Freeform, and iWorkneeds your help shaping the next generation of productivity tools by working on pioneering technologies to surprise and delight our users. As a Senior Machine Learning Data Scientist, you will be working alongside our world-class creatives, designers, scientists, and engineers to help innovate in the productivity space in ways that only Apple can. This is a highly visible, highly impactful opportunity!

As a Machine Learning Data Scientist, you'll be responsible for providing actionable insights to our research and post training teams that drive improvements to our core metrics and benchmarks. Your contributions will be critical to our success in delivering high-quality features to our customers.

Analyzing user feedback and telemetry to identify domain gaps between training and actual distributions.

Designing smaller-scale experiments to validate your hypotheses and inform further experimentation.

Developing tooling, presentations, diagrams, etc to effectively communicate the actionable insights you generate from your analysis.

Collaborating with our research and post training teams to inform additional model iteration and data collection.

At least 3 years experience with each of the following:

Contributing actionable insights to drive performance improvements in production environments.

Statistical analysis and modeling, including correlation analysis, clustering methods, probability theory, principal component analysis, outlier detection, and data visualization.

Using ML approaches for statistical analysis.

Bachelor's degree required in Computer Science, Machine Learning, Statistics fields, or a related subject area, or we may consider an equivalent industry experience.

Ability to distill complex data analysis into clear, user-focused recommendations and visualizations that drive product enhancements.

Proficiency with Python and numeric/statistical libraries like pandas, numpy, scipy, etc.

Strong English communication skills (both written and verbal).

MS or PhD in Computer Science, Machine Learning, Statistics fields, or a related subject area.

Understanding of how modern deep learning models work and how they're applied to solve user problems.

Experience with distributed computing frameworks (e.g., Spark, Hadoop) for large-scale data processing.

Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and ML deployment tools.

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 $147,400 and $272,100, 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.

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.

Apple accepts applications to this posting on an ongoing basis.