Applied Scientist

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 u0026 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. nnWe 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 youll have the rare and rewarding opportunity to work with datasets of unique magnitude richness and dedication to privacy that will frequently require novel approaches. Youll work alongside partners across Business Marketing Product Finance and Engineering daily to deliver material customer and business value.

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.

Engineer end-to-end scalable and robust Causal Inference products which provide Apple with an understanding of the health of our Services marketing deep into large-scale data sources to uncover opportunities for Causal Inference automation predictive methods and quantitative with product managers data scientists and other engineering teams to translate business requirements into technical specifications and deliver impactful practical solutions increasing internal adoption of causal inference approaches and democratizing datanStay abreast of the latest advancements in causal inference and AIML research evaluating and integrating new frameworks where appropriatenChampion best practices in software engineering MLOps code quality testing documentation and ensure compliance with data privacy and security

Masters degree in Statistics Economics Mathematics Machine Learning Computer Science Engineering or a related technical fieldn3 years of experience as an Applied Scientist Machine Learning or Data Scientist rolenFamiliarity with a brand 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 matchingnHands-on experience building Marketing Mix models and validation through Matched Market testingnSolid understanding of AIML technologies including Generative AInProven track record of successfully delivering complex projects from start to finishnProficiency in programming languages such as Python R SQL Java or CnExperience with cloud platforms Spark Docker and MLOps tools and best practicesnExcellent communication collaboration and presentation skills with meticulous attention to detail

PhD in related fieldnHands-on experience leveraging Generative AI to improve productivity and generate new insightsnCurious business attitude with an ability to condense complex concepts and models into clear and concise takeaways that drive action