Applied Scientist

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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