Applied Scientist

Apple Apple · Big Tech · Austin, TX +1 · Machine Learning and AI

Applied Scientist role at Apple focusing on causal inference, statistics, and machine learning to optimize marketing channels for consumer services. The role involves designing, developing, and deploying solutions using observational testing, counterfactual modeling, and lifetime value estimation, working with large-scale, privacy-focused datasets. Key responsibilities include engineering end-to-end causal inference products, analyzing data for automation and modeling opportunities, collaborating with cross-functional teams, and staying updated on research advancements.

What you'd actually do

  1. Engineer end-to-end scalable and robust Causal Inference products which provide Apple with an understanding of the health of our Services’ marketing efforts.
  2. Dive deep into large-scale data sources to uncover opportunities for Causal Inference automation, predictive methods, and quantitative modeling.
  3. 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
  4. Stay abreast of the latest advancements in causal inference and AIML research, evaluating and integrating new frameworks where appropriate
  5. Champion best practices in software engineering, MLOps, code quality, testing, documentation, and ensure compliance with data privacy and security

Skills

Required

  • Causal Inference
  • Statistics
  • Machine Learning
  • Python
  • R
  • SQL
  • Java
  • C++
  • Cloud platforms
  • Spark
  • Docker
  • MLOps

Nice to have

  • Generative AI
  • PhD

What the JD emphasized

  • 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

Other signals

  • optimize marketing channels
  • causal inference
  • machine learning
  • observational testing frameworks
  • counterfactual modeling
  • lifetime value estimation
  • customer acquisition and engagement challenges