Selection changes the language of the page/contentFinance Digital Transformation - Machine Learning EngineerCork, County Cork, Ireland Corporate FunctionsImagine 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.DescriptionThis 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.ResponsibilitiesFoundational knowledge of efficient data models for analyticsThe ability to build batch type, orchestrated data integrationsUnderstanding of data validations and automated monitoring to ensure integrity and consistency in data pipelinesLearning, Collaboration and CommunicationAbility to explain technical details to non-technical audiencesEffective working cross-functionally, understanding processAbility to translate an idea or problem into a solutionEager to collaborate with team members and business partnersEffective in or willingness to learn shell scriptingValues DRY principles, modularity, readability, supportability, and testingRealizes the difference between exploratory and production ready codeEffective writing SQL in data warehouse and cloud environmentsUnderstands and advocates version control and code reviewValues and understands process and data understanding as first principles versus iterating over algorithms and brute force solutionsMinimum QualificationsPractical experience applying, and theoretical understanding of machine learning algorithms and statistical methods for regression, classification, and outlier detectionExpertise in one or all domains is not required, the ability to learn and generalize is more importantExperience with the ML ops lifecycle – specifically as it relates to automated deployment, testing, concept drift monitoring and proactive model maintenanceGraduate 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 RPreferred QualificationsPrevious accounting experience or experience working in a corporate finance or accounting organizationUnderstanding of or ability to learn high level accounting principles, SOX and tax compliance and month-end close process
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