Applied Data Scientist

We are Earnix

We enable insurers and banks to provide faster, smarter, and safer prices and personalized products. With our products, insurers and banks can offer personalized value to every customer, every time, and in full alignment with their corporate business strategy, goals, and objectives.

With over 80 customers across the five continents, Earnix has been consistently innovating for Banks and Insurers around the globe. We have offices in the Americas, Europe, Asia Pacific, and Israel. ​

Description

The Analytics organization at Earnix is charged with developing state of the art analytical solutions & algorithms that are embedded into Earnix suite of products. We are continuously seeking to promote analytical and algorithmic innovation that address a broader set of business problems & challenges faced by Financial Institutions.

We are expanding our team and looking for an Applied Data Scientist who will be responsible for the development, testing and implementation of novel Machine Learning applications to enhance Earnix’s suite of products

Requirements

o MSc in Computer Science/Statistics/Engineering or any related field with focus on applied statistics, machine learning, deep learning

o At least 2 years’ practical experience in end-to-end implementation of ML models/algorithms and big data applications 

o Familiarity with machine learning/statistical/data-analysis algorithms: GLM, decision trees, GBM, clustering

o Proficiency in Python and Java

Advantages

o Experience in NLP and/or deep learning implementation

o Prior experience in the FinTech industry

o Experience with AWS products and services

o Experience with Web-development and front-end applications

o Familiarity with a wide range of analytical tools/ solutions, with a particular interest in Open Source solutions/tools

o Familiarity with containers & containers orchestration (Dockers)

Additional skills

o Fast learner & self-motivated

o Strong analytical capabilities

o Out of the box thinking

o Sufficient mathematical background for reading applied statistical/data-analysis papers

o English proficiency – both verbal & written

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