Applied Scientist

Apple Inc

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Work type: On-site
Location: Austin, TX
Posted: 179 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $202k

$131k $270k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $162,000–$241,906.

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 3497 open roles on FindRole.

Listed pay typically runs $174,149–$277,600 across 2702 roles with salary data.

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At a glance

TL;DR · Applied Scientist

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The Applied Scientist joins the Services Data Science & Analytics organization to develop AIML solutions that improve services while maintaining customer privacy. This role involves designing, developing, and deploying scalable Causal Inference products to optimize marketing channels through observational testing frameworks, counterfactual modeling, and lifetime value estimation. The scientist will engineer end-to-end products, dive into large-scale data sources for quantitative modeling, and define the technical roadmap for AIML initiatives. Key technical requirements 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, synthetic control, and propensity score matching, as well as knowledge of Generative AI to solve complex customer acquisition and engagement challenges.

What does a Applied Scientist earn?

Median $208800 from 61 postings across 10 companies.

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Skills

Causal Inference Machine Learning Generative AI Python R SQL Java C++ MLOps Spark Docker Statistics Counterfactual Modeling Marketing Mix Models

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.
Translate business requirements into technical specifications to deliver practical AIML solutions.
Define the technical vision, strategy, and execution roadmap for AIML initiatives.
Integrate the latest research and frameworks in causal inference and AIML into existing products.
Implement best practices in software engineering, MLOps, code quality, and data privacy compliance.
Build Marketing Mix models and perform validation through Matched Market testing.

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 (preferred).
3+ years of experience in an Applied Scientist, Machine Learning, or Data Scientist role.
Familiarity with quasi-experimental Causal Inference techniques like diff-in-diff, synthetic control, 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.
Proficiency in programming languages such as Python, R, SQL, Java, or C++.
Experience with cloud platforms, Spark, Docker, and MLOps tools and best practices.