This role pays less than
65%
of similar roles. Most pay
$211,612–$256,162
— the shaded band above.
At the midpoint, this role pays about
$214k
versus about
$234k
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
1984 open roles
on FindRole.
Listed pay typically runs
$175,000–$277,600
across 1590 roles with salary data.
Machine Learning Scientist - AppleCare WW Demand Planning will join the Worldwide Demand Planning team to operationalize and scale forecasting models. The role involves building robust, self-improving production systems that ensure service continuity by forecasting inventory for devices, repairable parts, packaging, and tools across global warehouses and retail locations. You will be responsible for moving models from research prototypes to production using CI/CD, APIs, and containerization while engineering novel features to solve complex demand challenges. The position requires expertise in Time Series forecasting, Anomaly Detection, and Optimization. Technical requirements include expert proficiency in Python with a focus on Object-Oriented Design, as well as advanced SQL skills for large-scale distributed data processing using frameworks like Snowflake, Oracle, Spark, or Hadoop. You must translate complex mathematical concepts into actionable insights for non-technical stakeholders to improve resource availability.
Develop and scale machine learning models to forecast inventory for devices, parts, and packaging.
Build robust, self-improving production systems using engineering best practices.
Create and deploy time series forecasting and anomaly detection models in real-world environments.
Write testable, maintainable Python code following object-oriented design principles.
Manage large-scale data processing using SQL and distributed frameworks like Spark or Snowflake.
Transition machine learning prototypes into production systems using CI/CD, APIs, and containerization.
Engineer novel features to solve complex demand challenges for global service centers.
Translate complex mathematical concepts into actionable insights for non-technical stakeholders.
What we're looking for
A Master's or PhD in Computer Science, Machine Learning, Statistics, Operations Research, or a related quantitative field with 3+ years of industry experience.
A Bachelor's degree in a quantitative field with 3+ years of industry experience in deploying Machine Learning models.
Practical experience creating and deploying models in real-world environments, specifically in Time Series forecasting, Anomaly Detection, or Optimization.
Expert proficiency in Python including software design principles, data structures, and writing maintainable code.
Expert-level SQL skills and experience with large-scale distributed data processing frameworks like Snowflake, Oracle, Spark, or Hadoop.
Proven experience taking models from research prototypes to production systems using CI/CD, APIs, and containerization.
Ability to translate complex mathematical concepts into clear insights for non-technical stakeholders.
Track record of up-skilling teammates by bridging the gap between statistical analysis and software engineering.
Machine Learning
Python
Java
C++
Spark
SQL
Snowflake
Hadoop
TensorFlow
Keras
PyTorch
Generative AI
RAG
Transformer Architecture
Deep Learning
Reinforcement Learning
Recommender Systems
Natural Language Processing
Distributed Systems
Machine Learning
Python
Java
C++
Spark
SQL
Snowflake
Hadoop
TensorFlow
Keras
PyTorch
Generative AI
RAG
Transformer Architecture
Deep Learning
Reinforcement Learning
Recommender Systems
Natural Language Processing
Distributed Systems
Machine Learning
Python
Java
C++
Spark
SQL
Snowflake
Hadoop
TensorFlow
Keras
PyTorch
Generative AI
RAG
Transformer Architecture
Deep Learning
Reinforcement Learning
Distributed Systems
Data Processing Pipelines
Machine Learning
Python
Java
C++
Spark
SQL
Snowflake
Hadoop
TensorFlow
Keras
PyTorch
Generative AI
RAG
Transformer Architecture
Deep Learning
Reinforcement Learning
Distributed Systems
Data Processing Pipelines