Applied Data Solutions Program, Machine Learning (Full-Time Opportunities)

Apple Inc.; Cupertino, Northern (CA, KY); On-site; USD 129,000 - 195,000; Full time

Job summary

Apple Inc. in Cupertino, California is offering the Applied Data Solutions Program with Machine Learning roles. You will build scalable data pipelines, data products, and end-to-end operational solutions across Apple to support BI and data science initiatives.

Roles include software engineering, data science, and ML engineering with rotations in Finance. You will deployment ML models, improve data infrastructure, and translate business challenges into technical outcomes within a collaborative

Qualifications

• Undergrad or grad degree in CS/DS/ML or related field.
• Proficient in Python/Scala/Java.
• Experience deploying ML solutions to real-world problems.
• Experience implementing data science apps in Python/Scala/Java.
• Excellent communication to translate complex concepts for business use.
• Previous Apple FDP or ADSP internship is required.

Responsibilities

• Support business partners by delivering data-driven solutions that mitigate fraud, improve security, and optimize efficiency.
• Collaborate with ML and software engineers to design scalable, user-friendly systems and tools.
• Translate ambiguous business problems into technical requirements and solutions.
• Design data-science driven solutions balancing traditional methods with custom approaches.
• Develop self-service tools to improve data engineering efficiency and analytics.
• Create reporting to monitor decision quality and business metrics.
• Develop, deploy, and support ML models for real-time and forensic threats to Apple’s ecosystem.
• Apply data science to finance processes to improve forecasting, accruals, entries prep, risk mitigation.

Skills

Python

Scala

Java

Communication

Education

Bachelor’s or Master’s in Computer Science/Data Science/Data Analytics/Machine Learning or related

Tools

Dataiku

Ray

GPU-based training environments

Job description

Applied Data Solutions Program, Machine Learning (Full-Time Opportunities)

Cupertino, California, United States Machine Learning and AI

Do you love the challenge of solving complex problems that can have a direct and meaningful impact on the company? Do you want to be part of a supportive team that’s constantly learning and having fun while solving tough business problems? We’d love to talk to you if you do!Join the Applied Data Solutions Program (ADSP) and make a positive impact on one of the most influential technology leaders in the industry. You’ll be tasked with building sophisticated, scalable and robust architecture, tools, data products, and critical data pipelines that are optimized for rapid business intelligence, data analysis, and data science. You will work with teams across Apple, using data analysis and predictive modeling techniques, to define, build, deploy, and maintain end-to-end operational solutions that have a direct and measurable impact to the company and our customers. The enormous scale and complexity of the problems and our data present exciting opportunities for pushing the limits of existing data science methods.

Description

The ADSP provides entry-level talent with practical work experience through project-based rotations on various teams within Finance. Upon completion, ADSPs move into non-rotating technical roles within Apple. We have a variety of opportunities for software engineers, data scientists, and machine learning engineers. Responsibilities may include:

• Support our business partners and optimizes the customer experience by delivering data-driven solutions that mitigate fraud, improve security, and optimize efficiency.
• Work closely with Machine Learning Engineers and other Software Engineers to lead the design and implementation of scalable, easy-to-use systems and tools.
• Engage with stakeholders to translate ambiguous business problems into technical solutions, including finding opportunities, breaking them into solvable segments, defining requirements, assessing level of effort, etc.
• Work cooperatively to design data science-driven solutions, balancing the utility of tried-and-true techniques and the benefits of custom solutions.
• Develop self-service tools and automation to improve data engineering efficiency and self-service analytics.
• Create reporting and monitor decisioning quality to maintain operational and business metric health.
• Translate ambiguous business problems into technical solutions by working collaboratively with business partners.
• Develop, deploy, and operationally support machine-learning models that take real-time and forensic action against a variety of threats to Apple’s ecosystem.
• Apply data science to core finance processes to automate and elevate capability, forecasting, accruals, accounting entries prep, IA process, risk mitigation, etc.

Minimum Qualifications

• Undergraduate or graduate degree in Computer Science, Data Science, Data Analytics, Machine Learning or related field.
• Proficient in at least one programming language (Python/Scala/Java preferred).
• Practical experience (acquired through work, independent projects, or academic research) in deploying machine learning solutions to answer real-world questions.
• Practical experience (acquired through work, independent projects, or academic research) with implementing data science-related applications in a programming language such as Python, Scala, or Java.
• Effective communication skills to translate complex concepts and analysis into concise, business-focused solutions.
• Previous Apple FDP or ADSP internship is required.

Preferred Qualifications

• Demonstrate ability to think holistically about system structures and interactions in order to anticipate technical, business, and customer impact.
• Experience with ML platform provisioning and optimization (e.g., Dataiku, Ray, GPU-based training environments).
• Demonstrate ability to think holistically about system structures and interactions in order to anticipate technical, business, and customer impact.

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $129,300 and $194,700, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant

At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong. Learn about accessibility in Apple’s workplace. Learn about reasonable accommodations for job applicants

Apple accepts applications to this posting on an ongoing basis.