The role is for a Data Scientist within Corporate Technology. The Corporate Technology Team is building out a dedicated new team in Bangalore, India for Risk and Finance Data Analytics.
The data-scientist will participate in all aspects of the software lifecycle, from systems design, development, testing, deployment and redesign after release. Located in Bangalore, he/she will be part of a globally distributed team, with technology partners in London, Glasgow and New York. In addition, the role includes extensive collaboration with our business partners as the financial analysts and controllers responsible for the firm’s valuation and valuation control.
The ongoing multi-year Risk and Finance Roadmap engineering programs within Corporate Technology has yielded a great opportunity for the firm to gain deep analytical data insights into the massive lakes of financial data.
Specifically the team is keen to identify data quality issues in massive amounts of financial information. The solution is expected to involve a mix of analytical processes including machine learning techniques as well as innovative visualization techniques. A key part of the analytics also requires good engineering solutions that help to incorporate the results of the analytics in the existing business workflow and capture user feedback on them to continuously improve the analytics.
This is an exciting opportunity to work on data driven analytical solutions and have a profound influence on the business processes of a major investment bank. The role requires holistic technology staff who can work with data ingestion, analytics and visualization and build customer focused solutions that infuse data analytics and machine learning methods in existing business processes.
Key Requirements of the Role:
· Experience in developing using Python, Java or Scala
· Experience in Big Data technologies e.g. Spark/Hive/HBase/Hadoop
· Good understanding of Maths, Statistics and the theoretical foundations of Machine Learning
· Familiarity in the implementation of Machine Learning algorithms is highly desired
· Proven track record in advanced Data Structures and Algorithms implementation (Python preferred, but Java/C++/Scala will be considered)
Additional Preferred Technical Skillsets:
Experience with SQL/NoSQL databases
Distributed Computing and Algorithm Optimization
Scikit-learn, MLLib and other Machine Learning libraries
Financial Engineering knowledge and familiarity with risk and regulatory data
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