Machine Learning Engineer - Financial Integrity - Risk

Key Skills

Aartificial intelligence, Cyber Security, Data Mining, Machine learning techniques, Mathematical modeling, PHP Programming, Python Programming, Regression pattern finding

Job Description

The Risk team in Financial Integrity is responsible for mitigating all fraudulent financial activity that is done in Meta products in order to drive trust and integrity for all people and businesses using Meta’s financial ecosystem, while accelerating growth. 

Fraud prevention is one of Meta’s leading investment areas as part of the financial activity expansion in our portfolio of products. As part of this, we are supporting highly scalable systems that are able to support tens of billions of transactions per year.

The team is composed of software engineers, machine learning engineers, data scientists which are mostly seniors in developing state of the art backend systems, supervised and unsupervised machine learning models and our focus is to continue to lead industry standards in the FinTech space.

Machine Learning Engineer - Financial Integrity - Risk Responsibilities

• Have a leading position in setting the direction and goals for the ML pillar, in terms of project impact, ML system design, and ML excellence.
• Creating and fostering long term vision on building first class, state of the art fraud enforcement solutions
• Owning the entire development cycle, from ideation to realization across the stack. Designing and developing machine learning models using supervised, unsupervised, and deep learning techniques.
• Take part in the overall team engineering efforts and contribute with hands-on work
• Working with software engineers to integrate machine learning models into applications and systems
• Collaborating with data scientists to identify and select appropriate machine learning algorithms and techniques

Minimum Qualifications

• Masters degree or above in Mathematics, Statistics, related technical field, or equivalent practical experience.
• Experience with developing machine learning models at scale from inception to business impact.
• Experience in one or more of the following areas: machine learning, classification, recommendation systems, pattern recognition, data mining, artificial intelligence, or a related technical field
• Strong communication skills and the ability to work effectively in a team environment

Preferred Qualifications

• Knowledge developing and debugging in PHP/Python.
• Experience with large-scale A/B testing systems, especially in the domain of financial institutions or cyber security companies