Applied Scientist

Apple Inc

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Austin, TX
Posted
134 days ago
Freshness
Confirmed live yesterday

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Salary context

How this pay compares to similar roles

Similar $191k
$129k most similar roles pay here $249k

This listing doesn't post a salary. Most similar roles pay $144,000–$237,250.

Based on 240 similar postings.

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About Apple Inc

Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software

Apple Inc currently has 1984 open roles on FindRole.

Listed pay typically runs $175,000–$277,600 across 1590 roles with salary data.

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At a glance

TL;DR · Applied Scientist

The Applied Scientist joins the Services Data Science & Analytics organization to develop innovative solutions at the intersection of causal inference, statistics, and machine learning. This role focuses on optimizing marketing channels through observational testing frameworks, counterfactual modeling, and lifetime value estimation. The successful candidate will design, develop, and deploy scalable Causal Inference products to measure marketing effectiveness while defining the technical vision for AIML initiatives. Day-to-day work involves analyzing large-scale data sources to automate predictive methods and translating business requirements into technical specifications. Required skills include proficiency in Python, R, SQL, Java, or C++, along with experience in Spark, Docker, and MLOps. The role requires expertise in quasi-experimental techniques like diff-in-diff and synthetic control methods, as well as knowledge of Generative AI to solve complex customer acquisition and engagement challenges within the services domain.

What does a Applied Scientist earn?

Median $208800 from 47 postings across 10 companies.

See salary data

What you'll do

  • Engineer end-to-end scalable and robust Causal Inference products to measure marketing effectiveness.
  • Analyze large-scale data sources to identify opportunities for automation, predictive methods, and quantitative modeling.
  • Develop counterfactual models and estimation techniques to optimize marketing channels and customer lifetime value.
  • Define the technical vision, strategy, and execution roadmap for AI/ML initiatives.
  • Translate complex business requirements into technical specifications and practical solutions for internal teams.
  • Integrate cutting-edge research in causal inference and machine learning into existing frameworks.
  • Implement MLOps best practices, including code quality, testing, and documentation.
  • Ensure all models and data processes comply with strict privacy and security standards.

What we're looking for

  • Master's degree in Statistics, Economics, Mathematics, Machine Learning, Computer Science, Engineering, or a related technical field.
  • PhD in a related field is preferred.
  • 3+ years of experience as an Applied Scientist, Machine Learning, or Data Scientist.
  • Familiarity with quasi-experimental Causal Inference techniques such as diff-in-diff, synthetic control, and regression discontinuity design.
  • Hands-on experience building Marketing Mix models and validation through Matched Market testing.
  • Solid understanding of AIML technologies including Generative AI.
  • Proficiency in programming languages such as Python, R, SQL, Java, or C++.
  • Experience with cloud platforms, Spark, Docker, and MLOps tools and best practices.

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