Applied Scientist
Confirmed on the employer's own hiring board on Oct 22, 2026. First seen by Alion on Apr 9, 2026. Apple scores A on the Alion truth index.
Services at Apple help hundreds of millions of customers get the most out of the devices they love through amazing apps, award-winning shows and movies, immersive music in spatial audio, world-class workouts and meditations, super fun games and more! The Services Data Science & Analytics organization is passionate about developing discerning insights and AIML solutions to help continually improve these services and accelerate growth while maintaining a strong dedication to customer privacy.
We are currently seeking an experienced and passionate Applied Scientist, who will work on innovative products at the intersection of causal inference, statistics, and machine learning to help optimize marketing channels, via observational testing frameworks, counterfactual modeling, and lifetime value estimation. As a key member of our diverse organization, you'll have the rare and rewarding opportunity to work with datasets of unique magnitude, richness, and dedication to privacy that will frequently require novel approaches. You'll work alongside partners across Business, Marketing, Product, Finance, and Engineering daily to deliver material customer and business value.
Description
As an Applied Scientist, you will have the responsibility of pushing the boundaries of how Causal Inference and AIML can be leveraged to better serve our customers. You will be at the forefront of designing, developing, and deploying cutting-edge Causal Inference solutions, that directly impact our products and provide a granular understanding of key marketing effectiveness. You will also be instrumental in defining the technical vision, strategy, and execution roadmap for our AIML initiatives, ensuring that we deliver high-quality, scalable, and impactful models that solve complex customer acquisition and engagement challenges. You will also be a key driver in fostering a vibrant culture of innovation, continuous learning, and collaborative problem-solving.
Minimum Qualifications
Master’s degree in Statistics, Economics, Mathematics, Machine Learning, Computer Science, Engineering, or a related technical field
3+ years of experience as an Applied Scientist, Machine Learning, or Data Scientist role
Familiarity with a broad range of quasi-experimental Causal Inference techniques such as diff-in-diff, synthetic control method, panel analysis, regression discontinuity design, interrupted time series, and propensity score matching
Hands-on experience building Marketing Mix models and validation through Matched Market testing
Solid understanding of AIML technologies including Generative AI
Proven track record of successfully delivering complex projects from start to finish
Proficiency in programming languages such as Python, R, SQL, Java, or C++
Experience with cloud platforms, Spark, Docker, and MLOps tools and best practices
Excellent communication, collaboration, and presentation skills with meticulous attention to detail
Preferred Qualifications
PhD in related field
Hands-on experience leveraging Generative AI to improve productivity and generate new insights
Curious business attitude with an ability to condense complex concepts and models into clear and concise takeaways that drive action