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Applied Scientist
AppleAustin, Texas, United States3+ years
Apply 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.
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 efforts.
- Dive deep into large-scale data sources to uncover opportunities for Causal Inference automation, predictive methods, and quantitative modeling.
- Collaborate 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 data
- Stay abreast of the latest advancements in causal inference and AIML research, evaluating and integrating new frameworks where appropriate
- Champion best practices in software engineering, MLOps, code quality, testing, documentation, and ensure compliance with data privacy and security
- 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 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 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
- 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
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