Do you have what it takes to join Channel VAS, the global Fin Tech leader?
Channel VAS lends more than $4.5 million USD to over 550 million people in 25 countries worldwide - and these numbers keep growing every day! We employ only the best to join our team of highly skilled professionals in our Operations Center in Greece and other countries.
Data Scientists are significant contributors of Channel VAS’ data driven automated decision making and risk management. They have extensive experience with machine learning, and the development and deployment of advanced algorithms.
They have the capability to (i) perform advanced analytics, (ii) design and develop statistical and machine learning algorithms, and (iii) operationalize them in credit risk management.
Data Scientists are part of a large team of 15 people, working closely with Big Data Engineers and Analysts.
Design and implement credit risk models and algorithms beyond the state of the art.
Develop and deploy advanced profit scoring models.
Identify credit risk factors by applying computational methods to large data volumes.
Apply deep and ensemble learning to optimize risk models.
Determine optimal risk strategies through computational means.
Deliver credit-risk insights through big data risk analytics.
Required Experience & Qualifications :
BSc and MSc in Mathematical Sciences or Computer Science from an accredited institution.
Hands-on experience at least in two of the following (with descending significance) : Machine learning and AI Credit risk models Big-
Data; Apache SPARK Risk Analytics Predictive Analytics Mathematical & Statistical modelling
Ability to efficiently search and understand the scientific literature of mathematical models, machine learning and AI.
Ability to judge the relevance of existing models and algorithms to specific business needs.
Strong analytical skills.
Excellent judgment and problem-solving skills.
Passion for learning, exploring and developing new models and machine learning and AI algorithms.
Ability to hit tight deadlines and work under pressure and strict attention to detail.
Optional / Will be considered a plus :
PhD in Computer Science, Mathematical Sciences or Finance from an accredited institution.
Hands-on experience of big data processing and analytics.
Hands-on experience of data bases (SQL and NoSQL).