Data Scientist at EXUS Innovation (London)

EXUS Innovation manages a portfolio of initiatives that aim to pave the way for the introduction and take up of emerging technologies. Exus excel in driving innovation in software engineering and data management to foster advances in key sectors such as: security, health, creativity and lifelong learning. Already having a team of top engineers and computer scientists, and due to continued growth of their research portfolio, they would like to enrich their team’s expertise with an experienced Data Scientist. This role is located in London.

Main responsibilities:
- Discover and communicate meaningful patterns from large data sets and from a variety of domains (healthcare, finance, demographics).
- Employ elements of artificial intelligence, machine learning, statistics, and database systems to develop analytic methodologies.
- Analyze the effectiveness of existing predictive models and optimization strategies.
- Deal with changing priorities/workloads with a positive attitude
- Express technical ideas in an understandable and engaging way, verbally and in writing (in English).
- Continuously keep current with emerging technologies and business trends.

Qualifications:
- Excellent academic background from a top-tier university with background in mathematics, statistics, engineering or related discipline. A relevant doctorate degree is desirable but not essential.
- Working experience 1-3 years in a similar position will be considered an asset.
- Demonstrated ability to work with standard database query, data manipulation tools or related programming languages.
- Familiar with any of those databases- SQL Server, Oracle, Netezza, Teradata, MySQL
- Expertise in R, MATLAB, SPSS, SAS or equivalent
- Experience of technology platforms - Hadoop, Hive and Map/Reduce
- Experience in Hadoop platforms (Cloudera, BigInsights, MapR, HortonWorks)
- Experience in a combination of the following modeling methods: Linear Regression, Partial Least Square Regression, Logistics Regression, Survival analysis, Repeated measures analysis, Principle component analysis, Decision tree, Support Vector Machine, k-means clustering, Neural Networks

More information: http://goo.gl/3lOZZf