Applied Scientist

Apple Inc

Quick summary

Work type: On-site
Location: Culver City, CA
Salary: $171,600–$302,200 / yr
Posted: 42 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $179k

This role $237k

$106k most similar roles pay here $323k

This role pays more than 80% of similar roles. Most pay $126,800–$230,306 — the shaded band above. At the midpoint, this role pays about $237k versus about $179k for comparable roles.

Based on 240 similar postings.

Employer

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 1723 open roles on FindRole.

Listed pay typically runs $162,500–$272,100 across 1398 roles with salary data.

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At a glance

TL;DR · Applied Scientist

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The Applied Scientist role within the Services Data Science & Analytics team at a leading tech company involves developing innovative causal inference solutions to optimize marketing channels and enhance customer acquisition strategies. This senior position requires designing, implementing, and deploying cutting-edge models using large-scale datasets to provide granular insights into marketing effectiveness. Daily tasks include collaborating with cross-functional teams to translate business needs into technical specifications, ensuring the adoption of causal inference methods across the organization. The ideal candidate will have a Master’s degree in a relevant field, 3+ years of experience, and expertise in various quasi-experimental techniques such as diff-in-diff and propensity score matching. Proficiency in Python, R, SQL, Java, or C++, along with familiarity with cloud platforms like Spark and Docker, is essential for delivering scalable solutions that drive business growth while maintaining strict data privacy standards.

Skills

Python R SQL Java C++ AWS Spark Docker MLOps CI/CD Generative AI Causal Inference Marketing Mix Models Quasi-experimental Techniques Diff-in-diff Synthetic Control Method Panel Analysis Regression Discontinuity Design Interrupted Time Series Propensity Score Matching

What you'll do

Develop and deploy scalable Causal Inference solutions for marketing effectiveness.
Identify opportunities for automation in large-scale data analysis using predictive methods.
Translate business requirements into technical specifications for causal inference projects.
Evaluate and integrate new frameworks to advance AIML initiatives and solve complex challenges.
Champion software engineering best practices, ensuring compliance with privacy and security standards.

What we're looking for

Master’s degree in a relevant technical field such as Statistics, Economics, or Computer Science.
3+ years of experience as an Applied Scientist or similar role.
Expertise in various quasi-experimental Causal Inference techniques and Marketing Mix models.
Strong understanding of AIML technologies including Generative AI.
Proficiency in Python, R, SQL, Java, or C++ for data analysis and modeling.
Experience with cloud platforms, Spark, Docker, and MLOps tools.
Excellent communication skills and ability to translate complex concepts into actionable insights.

Applied Scientist

Opendoor

Miami, FL 31 days ago $156,800–$335,000

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