CT Data Scientist

The selected candidate

• Develops and reviews complex data analytics models, algorithms, systems and solutions, using advanced and specialist data science, programming and analytical techniques

• Delivers complex predictive models and identifies new projects, opportunities and data sources which can be used to drive measurable client and commercial benefit

• Provides insights and recommendations from different data sources to senior team members to help make decisions that drive better business outcomes for clients 

Your key responsibilities include

• Working closely with senior team members to scope, identify and establish appropriate data analysis methodologies for projects

• Seeking new data sources internally and externally that enable leading edge data science solutions

• Developing complex statistical modelling tools that drive business outcomes for clients, involving complex algorithms, artificial intelligence methods, machine learning, deep learning and advanced statistics

• Confidently using visual tools to communicate data insights and linking data to business outcomes

 Skills and attributes for success

· Computer Vision - Embed computer vision techniques which enables automation of human visual system through high-level understanding from digital images and videos

· Data Visualization and storytelling - Capability to assimilate and present data, as well as more advanced visualization techniques. Capability to engage and explain data creating a narrative around data visualization

· Deep Learning - Capability to perform machine learning applications that do unsupervised learning from unstructured data

· Machine Learning - Capability to develop and use algorithms that analyze a data set, which then self-learn from that Analysis to improve the predictions made by these algorithms. The more often the algorithm is run / optimized, the more it learns and improves. This can then be used to make data-driven predictions

· Mathematics & Statistics - Advanced knowledge in mathematics and statistics. Knowledge in methods and models applied to solve problems with large datasets

Natural Language Processing & Generation - Capability to perform applications combining Artificial Intelligence and Automation to collect and process large amounts of data to automate entire processes or workflows

· Predictive Analytics - Capability to develop and use algorithms on current or historic data sets to predict future outcomes based on that data set

· Prescriptive Analytics - Capability to develop and use algorithms on a current or historic data set to predict future outcomes based on that data set AND to recommend actions / decisions that should be taken based on those predictions

· Real-time Analytics - Capability to deliver analytics, of any type (descriptive, prescriptive, predictive), on a dataset using data that has been analyzed and presented in real-time

· Reasoning and Knowledge Representation - Determine use cases and organizational design to embed reasoning and knowledge representation into defined client function

· Unstructured Data Analytics - Capability to deliver analytics, of any type (descriptive, prescriptive, predictive), on a dataset using unstructured data

Intermediate understanding of some of the following tools and technologies, and how to apply them to solve business problems

· Business Information Glossaries - Azure Data Catalogue, Business Objects, Collibra,

IBM Business Glossary, SAP Information Steward

· Data Visualization Tools - D3.js, Design Studio, Einstein Analytics, Power BI, Qlik, SAP

Lumira, Spotfire, SSRS, Tableau, Zeppelin

· Graph Databases - AWS DynamoDB, Azure Cosmos, DataStax Enterprise Graph, IBM

Graph, InfiniteGraph, Neo4j, Teradata Aster, Titan

· Programming Languages - .Net, C#, C++, CSS, Hive, HTML5, Java, MATLAB, Node.js,

Pig, PowerShell, Python, R, Ruby, Sass, Scala, Shell Scripting, SQL, Visual Studio, XML

· Relational SMP Databases - Azure SQL PaaS, IBM DB2, MySQL, Oracle, PostgreSQL,

SQL Server

· Statistical Modelling Tools - SAS, SPSS

· Third Party AI Tools - Amazon Web Services Machine Learning API, Azure Cognitive

Services, Caffe, CNTK, Google API, Google TensorFlow, Hypatos SMACC, Keras,

Microsoft Distributed Machine Learning Toolkit, Theano 

Education

· B.S. in Actuarial, Behavior Economics, Computer Science, Mathematics, Economics, Engineering, IT, Cyber Security, or related field preferred 

What we look for

• Strong analytical skills and problem-solving ability

• A self-starter, independent-thinker, curious and creative person with ambition and passion

• Excellent inter-personal, communication, collaboration, and presentation skills

• Customer focused

• Excellent time management skills

• Positive and constructive minded

• Takes responsibility for continuous self-learning

• Takes the lead and makes decisions in critical times and tough circumstances

• Attention to detail

• High levels of integrity and honesty