Finance Data Scientist – Accounting and Risk

Apple • Cupertino, CA

3h

About The Position

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.

Requirements

• 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
• 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
• 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
• 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
• 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

Nice To Haves

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level