Data Science Analyst – Analytics and Automation

London Stock Exchange Group is a global markets infrastructure business, sitting at the heart of the world’s financial community. We provide valuable services for a wide range of customers, focusing on Intellectual Property, Risk and Balance Sheet Management and Capital Formation.

- Strong problem solving skills with an emphasis on product development;
- Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets;
- Experience working with and creating data architectures;
- Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
- Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications;
- Excellent written and verbal communication skills for coordinating across teams;
- A drive to learn and master new technologies and techniques;
- Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
- Experience querying databases and using statistical computer languages: R, Python, SLQ, etc;
- Experience using web services: Redshift, S3, Spark, DigitalOcean, etc;
- Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc;
- Nice to have experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc;
- Nice to have experience in visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot, etc.

- Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions;
- Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies;
- Assess the effectiveness and accuracy of new data sources and data gathering techniques;
- Develop custom data models and algorithms to apply to data sets;
- Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes;
- Develop company A/B testing framework and test model quality;
- Coordinate with different functional teams to implement models and monitor outcomes;
- Develop processes and tools to monitor and analyze model performance and data accuracy.