Job Information
Meta Machine Learning Engineer - Financial Integrity Risk in Tel Aviv, Israel
Summary:
The Financial Integrity (FI) Risk team is part of the Meta FinTech (MFT) engineering teams dealing with Risk, Compliance and Care (AKA Financial Integrity). The team is responsible for detecting and stopping financial risk (e.g. fraud) throughout the customer and business journey.Our Tech:We are using a variety of ML and stats models and algorithms, from classic supervised learning, to anomaly detection, unsupervised learning and semi supervised learning, to detect and mitigate ongoing fraud campaigns.In our solutions we are using Graph Neural Networks, Ensembles, Boosting, Regression models, Time Series data, Causal Inference and more.
Required Skills:
Machine Learning Engineer - Financial Integrity Risk Responsibilities:
Play a role in setting the direction and goals for the ML pillar, in terms of project impact, ML system design, and ML excellence.
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.
Implementing and testing machine learning models in a production environment
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:
Minimum Qualifications:
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 Knowledge developing and debugging in PHP/Python.
Experience in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining, artificial intelligence, or a related technical field.
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Preferred Qualifications:
Preferred Qualifications:
Experience with large-scale A/B testing systems, especially in the domain of financial institutions or cyber security companies
Masters degree in Mathematics, Statistics, related technical field, or equivalent practical experience.
Python and PHP/Hack experience
Industry: Internet