Finance Digital Transformation - Machine Learning Engineer

Summary 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 machine learning engineer in Finance, you’ll play an integral and global role in building the data foundations, services, and platforms used for delivering insights and automating decisions for Apple’s Finance organisation. Description This role will require you to be collaborative by learning intra-team and business process in order to build infrastructure and services to enable an effective Machine Learning practice. You will help lead the charge by developing a strong ML Ops process in a dynamic Finance environment where you will deal with unique challenges specific to Finance organisations, such as SOX and regulatory compliance. Your ability to instill and proliferate strong software engineering practices into team data science and machine learning processes will be critical. You are a quantitatively and technically inclined individual with an applied data science and/or software engineering background. A good understanding of data engineering principles is important as you will often be responsible for creating your own data models or working with data engineering to optimize internal team frameworks and services. A love for testing, validation and configuration as code will set you apart. You are not required to be an expert in one field, rather, your ability to learn and problem solve is much more desirable. Additionally, the ability to partner and share your expertise with others will help you succeed. Responsibilities

• 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
• 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

Minimum Qualifications

• 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
• Experience with the ML ops lifecycle – specifically as it relates to automated deployment, testing, concept drift monitoring and proactive model maintenance
• Graduate degree in economics, computer science, mathematics, quantitative finance, or other quantitative discipline with three years of experience.
• Undergraduate degree in finance, economics, accounting or related business discipline with five years demonstrated experience in data science applications and programming in Python and/or R

Preferred Qualifications

• Web application development experience in react/python
• 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