We are pleased to announce the position of a Data Scientist in the Big Data and Business Analytics Department within the Finance Division. In keeping with our current business needs, we are looking for a person who meets the criteria indicated below.

Brief Description

Reporting to the Lead Data Scientist, the position holder will be responsible for turning terra-bytes of data into actionable insights and new products for our customers. As a data scientist you will be working side by side with designers, product managers and our global partners from industry and academia. We expect you to take full ownership of your work, from conception all the way to the final product on our platform. Our ideal candidate must be result-oriented, with great ideas and solid quantitative skills to turn ideas into reality. You should have solid machine learning and analytics skills.

Key Responsibilities

Selecting features, building and optimizing classifiers using machine learning techniques
Processing, cleansing, and verifying the integrity of data used for analysis.
Collaborate with business units and engineering teams to understand and prioritize company needs and devise possible solutions based on business use cases
Create various machine learning-based tools or processes within the company, such as Credit scorecards, recommendation engines or automated lead scoring systems to drive revenue or create cost efficiencies.
Create visualizations using state of the art visualization tools.
Work in a multi-disciplined team and take ownership of turning ideas into machine learning models.
Produce products or improve existing products with short turnaround times.
Solid development and analytical approaches to create viable implementations.
Bring solid credit risk analytics skills that will contribute to the development of the credit lifecycle management craft

QUALIFICATIONS

BSC or MS in quantitative field such as computer science, Statistics, Mathematics, Actuarial Science, Engineering or equivalent practical experience
3 – 5 years data science working experience.
Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, Neural Networks
Experience with common data science programming languages, such as Python, R, Spark, MatLab
Experience with data visualization tools, such as Qclick, Tableau, PowerBI
Experience with Relational and NoSQL databases, such as Oracle, SQL Server, PostgreSQL, MongoDB, Cassandra, HBase
Very solid applied statistics knowledge
Good understanding of big data technologies like Hadoop
Strong communications and interpersonal skills and quick grasps to understand business problems Understanding of SQL and databases.