- –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
- –Excellent interpersonal and collaboration skills
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