As part of our strategy for future growth, we are creating a state of the art Data Science Team that builds models on the vast data of our market leading Open Banking platform. Compared to other technology companies of similar size, the set of problems that we tackle is incredibly diverse, cutting across optimization, prediction, modelling, inference and mapping. If you are excited about extracting information from data to tackle these problems within a fast-paced, innovative environment and are passionate about analytics with the innate ability to assimilate large amounts of complex information making it both simple and actionable, then this is the job for you.
- Provide stakeholders with the ability to make decisions and gain insights through data visualization and reporting.
- Own prototyping efforts for demonstration and illustration purposes to peer groups, Business partners, or senior leaders.
- Drive the creation of new models and capabilities that will leapfrog traditional modelling done at most major financial institutions leveraging a wide array of data from both traditional and non-traditional sources.
- Perform data integration, manipulation, and querying for purposes of reporting and more sophisticated analytics.
- Engage in regular problem-solving sessions with stakeholders to present findings and refine their own analytics plan.
- Develop database objects, including tables, stored procedures, views, triggers, keys, and declarative integrity constraints.
- Mine Big Data and other unstructured data to tap untouched data sources and deliver insight into new and emerging solutions.
- Remain current on new developments in data analytics, Big Data, predictive analytics, machine learning and technology.
- Must have hands-on experience with a BI Solution like Tableau, D3, Microstrategy, Power BI, Looker, etc.
- Must have strong SQL experience.
- Must have experience in either R or Python and/or Weka, NumPy, MatLab.
- Proficiency in using other query languages such as Hive, Pig will be advantageous.
- Experience with data warehousing technologies such as ETL, OLAP cubes, multi-dimensional modelling, data cleansing, and others.
- Experience in application of techniques such as gradient boosting, random forest, neural networks is desirable and exposure to building AI based solutions will be a positive.
- Excellent communication skills in English.