Machine Learning Engineer - Product Marketing Customer Analytics

  • Cupertino, CA
  • Posted 30 days ago | Updated 6 hours ago

Overview

On Site
USD 207,800.00 - 312,200.00 per year
Full Time

Skills

Customer Analysis
Analytical Skill
Product Marketing
Investor Relations
Predictive Analytics
Product Requirements
Modeling
Leadership
Machine Learning Operations (ML Ops)
Deep Learning
GPU
CPU
Management
Data Quality
Algorithms
Predictive Modelling
Visualization
Machine Learning (ML)
Collaboration

Job Details

Summary
At Apple, new ideas have a way of becoming excellent products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish! The Product Marketing Customer Analytics team is seeking a Machine Learning Engineer with deep technical experience in predictive analytics and analytic engineering.

Description
Support Product Marketing, Investor Relations, and the Executive Team with predictive analytics for customer product and services engagement. Understand product requirements then translate them into modeling tasks and engineering tasks
Develop scalable ML algorithms and models to understand customer behavior and provide leadership with actionable insights and recommendations
Design and implement end-to-end machine learning pipelines-from feature engineering to model serving- using best in class MLOps frameworks
Develop and optimize deep learning and traditional ML solutions on high-volume datasets using GPU clusters or distributed CPU environments.
Experiment with cutting-edge algorithms, providing advanced insights into customer behavior and engagement.
Manage ML projects through all phases, including data quality, algorithm/feature development, predictive modeling, visualization, and deployment and maintenance.
Tackle difficult, non-routine analysis/prediction problems, applying advanced ML methods as needed.
Partner with peers to build and prototype analysis pipelines that provide insights at scale.
Collaborate with data engineers and infrastructure partners to implement robust solutions and operationalize models. Enhance and evolve solutions to meet changing business needs with agility.
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