Logo of Huzzle

Finance Machine Learning Engineer

image

Apple

4d ago

  • Job
    Full-time
    Senior Level
  • Software Engineering
  • Austin

AI generated summary

  • You need 5+ years of experience, a Bachelor's in Computer Science or a related quantitative field, strong Python skills, ML ops expertise, knowledge of machine learning algorithms, data modeling, data validations, accounting experience, and understanding of accounting principles.
  • You will collaborate to build ML infrastructure, develop strong ML Ops processes, apply data science and software engineering skills, optimize data models and services, and share expertise with others.

Requirements

  • 5+ Years of relevant experience
  • Bachelor's in Computer Science or other related quantitative field
  • Preferred Qualifications:
  • Efficient python (or equivalent scripting language) programmer experience
  • Effective writing SQL in data warehouse and cloud environments
  • Experience with the ML ops lifecycle – specifically as it relates to automated deployment, testing, concept drift monitoring and proactive model maintenance
  • Practical experience applying, and theoretical understanding of machine learning algorithms and statistical methods for regression, classification, and outlier detection
  • Foundational knowledge of efficient data models for analytics and the ability to build batch type, orchestrated data integrations
  • Understanding of data validations and automated monitoring to ensure integrity and consistency in data pipelines
  • Previous accounting experience or experience working in a corporate finance or accounting organization is a plus.
  • Understanding of or ability to learn high level accounting principles, SOX and tax compliance and month-end close process is a plus.

Responsibilities

  • 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.

FAQs

What qualifications are required for the Finance Machine Learning Engineer position?

Minimum qualifications include having 5+ years of relevant experience and a Bachelor's degree in Computer Science or a related quantitative field.

What are the preferred qualifications for this role?

Preferred qualifications include being an efficient Python programmer, proficient in writing SQL, experience with ML ops lifecycle, familiarity with machine learning algorithms, knowledge of data models for analytics, understanding of data validations, and previous accounting or finance experience.

What specific challenges might a Finance Machine Learning Engineer face in this role?

Challenges may include dealing with unique Finance-specific challenges such as SOx and regulatory compliance, applying machine learning in a Finance environment, and integrating software engineering practices into data science processes.

What skills are important for success in this role?

Skills such as problem-solving, ability to learn quickly, collaboration, data engineering principles, software engineering practices, testing, validation, efficient data models, and knowledge of accounting principles are important for success as a Finance Machine Learning Engineer.

Technology
Industry
10,001+
Employees
1976
Founded Year

Mission & Purpose

We’re a diverse collective of thinkers and doers, continually reimagining what’s possible to help us all do what we love in new ways. And the same innovation that goes into our products also applies to our practices — strengthening our commitment to leave the world better than we found it. This is where your work can make a difference in people’s lives. Including your own. Apple is an equal opportunity employer that is committed to inclusion and diversity.