–Collaborate with, developers, security traders, and other members of the research team to develop new and improve existing algorithms that optimize capital markets performance.
–Utilize combination of statistical analysis, data mining and deep learning techniques to improve system development and research effectiveness
–Apply leading-edge machine learning techniques to large financial data sets and optimize quantitative research performance
–Data Exploratory Analysis.
–Lead the development of new Predictive models working with internal stakeholders from gathering requirements to delivery.
–Perform data mining by applying machine learning and supervised learning algorithms.
–Support advanced analytical and data mining efforts which could include but not limited to clustering, segmentation, logistic and multivariate regression, decision/CART trees, neural networks, time-series analysis, sentiment analysis, topic modeling, and Bayesian analysis
–Visualize, interpret, report, and communicate data findings creatively in various formats and audiences using Tableau, ggplot. • Integrate with cloud-based web technologies via web services API.
–Think creatively and work well both as part of a team and as an individual contributor.
–Responsible for defining the structures of solutions and architectures to address client business problems.
–The data scientist understands client needs and business constraints.
–Works in levels of abstraction and applies industry knowledge and leverages appropriate business elements, machine learning and information technology to address those needs.
–Bachelor Degree in Machine Learning, Statistics, Applied Mathematics, Computer Science, Information Systems, or related quantitative disciplines, with a minimum of five years of relevant experience (Bachelor’s required, Master’s or Ph.D.’s preferred)
–Expert in analytical tools like R, SAS, SQL.
–Have hands-on experience developing predictive models using analytical methods such as regression, decision trees, support vector machines, Random Forests, Neural Networks.
–Have hands-on experience using relational database management systems (Oracle, Teradata, SQL Server, DB2 etc.)
–Hands on experience in Java, Hadoop, HIVE, Scala, Apache Spark, and MLLib – to include developing Machine Learning Algorithms in Spark MLLib and building real-time models preferably in Spark MLLib.
–Expertise in visualization tools like Tableau, ggplot is a plus.
–Expertise in Python, Linux is a plus.
–Exceptional ability to communicate and present findings clearly to both technical and non-technical audiences