Data Engineering, Data pipelines, Finance, Machine learning techniques, Optimization, SQL
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 organizations, 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.
Minimum Qualifications
Preferred Qualifications
Headquartered in Cupertino, California, Apple Inc. is a multinational technology company that focuses on producing consumer electronics, software, and online services. It holds the distinction of being the world's largest technology company by revenue and the world's biggest company by market capitalization as of June 2022. Apple is the second-largest mobile phone manufacturer and the fourth-largest personal computer vendor by unit sales.
Munich, Bavaria, Germany
4-6 year
Sunnyvale, CA, USA
12-14 year
Cupertino, CA, USA
8-10 year