In our solutions we are using Graph Neural Networks, Ensembles, Boosting, Regression models, Time Series data, Causal Inference and more.
Machine Learning Engineer – Financial Itegrity 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
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
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
