Imagine what you could do here. At Apple, new ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and curiosity to your job and there's no telling what you could accomplish. Do you love thinking analytically? Just as our customers find value in Apple products, the Finance group finds value for both Apple and its shareholders.

As a Finance Data Scientist, you'll play an integral and global role in building the data foundations, delivering insights, and automating decisions for Apple's Record to Report, Risk, and accounting teams.

The Finance Data Scientist is typically an applied generalist and is not asked to be an expert in all things. Rather, the Finance Data Scientist is an effective partner who is eager to learn and apply analytical solutions to varying set of problems.

Description

This role requires you to be consultative to learn business process, generate new ideas, apply analytic methods, and generally collaborate effectively with partners in Record to Report, Internal Audit and Risk. You will deal with disparate data and must be able to acquire, transform, analyze, and deliver results from curated sources as well as from difficult to manage, unstructured data sources. You will help to uncover meaningful patterns, build intelligent solutions, deliver critical insights, and provide decision support for your business partners. Your focus will be to identify, triage and reduce financial risk and automate business process. Creativity, collaboration, and drive are required to unearth features and signals in the vast amount of data inside and outside of Apple.

You are a quantitatively and technically inclined individual with an applied data science background. A good understanding of data engineering principles is important as you will often be responsible for creating your own data models. You are not required to be an expert in one field, rather, your ability to learn and problem solve sets you apart. A desire to partner with data engineers, application developers and business partners is critical. Additionally, the ability to share your expertise with others will help you succeed.

Preferred Qualifications

Graduate degree in economics, computer science, mathematics, quantitative finance, or other quantitative discipline with two years of experience.

Previous accounting experience or experience working in a corporate finance or accounting organization

Understanding of or ability to learn high level accounting principles, SOX and tax compliance and month-end close process

Experience with the model ops and ML ops lifecycle - specifically as it relates to concept drift monitoring and proactive model maintenance

Minimum Qualifications

Undergraduate degree in finance, economics, accounting or related business discipline

5 years demonstrated experience in data science applications and programming in Python and/or R

Data Engineering Fundamentals:

Foundational knowledge of efficient data models for analytics

The ability to build batch type, orchestrated data integrations

Understanding of data validations and automated monitoring to ensure integrity and consistency in data pipelines

Learning, Collaboration and Communication:

Ability to explain technical details to non-technical audiences

Effective working cross-functionally, understanding process

Ability to translate an idea or problem into a solution

Eager to collaborate with team members and business partners

Experience with creating effective, clean visualizations to communicate analytical results to different audience types

Programming Fundamentals:

Efficient python (or equivalent scripting language) programmer

Effective in or willingness to learn shell scripting

Values DRY principles, modularity, readability, supportability, and testing

Realizes the difference between exploratory and production ready code

Effective writing SQL in data warehouse and cloud environments

Understands and advocates version control and code review

Analytics Fundamentals:

Values and understands process and data understanding as first principles versus iterating over algorithms and brute force solutions

Experience in applying standard grouped and longitudinal testing with an understanding of sampling and design methods and structures

Practical experience applying, and theoretical understanding of machine learning algorithms and statistical methods for regression, classification, and outlier detection

Expertise in one or all domains is not required, the ability to learn and generalize is more important

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 .

Pay & Benefits

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 $172,100 and $258,600, 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.