The Corporate Data and Analytics Services (CDAS) Elite AIML organization, solves challenging business problems using data science and machine learning techniques across Corporate technology and the supported Corporate Functions.

As a Data Scientist in this team will help build robust and scalable solutions for various banking domains, and also enable the services to be deployed and integrated into existing production business workflows.. This is an exciting opportunity to work on data driven analytical solutions within an advanced ML team and have a profound influence on the business processes of a leading global bank.  

Job Responsibilities

Help to protect the firm by developing new insights that will support senior management in making key country decisions Become an expert in the data used by the team, including the ability to efficiently and creatively transform that data into insights Develop potential machine learning use cases for the team, to apply to tasks such as data analytics, NLP, time-series predictions or recommendation systems Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production

Required Qualifications, Capabilities, And Skills

Master’s degree in a data science-related discipline, plus at least four years of industry experience (or: PhD in a data science-related discipline, plus at least two years of industry experience) Extensive experience with data transformation (especially in Python) and analytics Experience with continuous integration models and unit test development Strong written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences. Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments Curious, hardworking and detail-oriented, and motivated by complex analytical problems

Preferred Qualifications, Capabilities, And Skills

Familiarity with the financial services industry and Risk management Background in NLP and analytics, personalization/recommendation Knowledge in search/ranking, Reinforcement Learning or Meta Learning Experience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large scale distributed environment and ability to develop and debug production-quality code Some industry experience in implementing machine learning and deep learning toolkits (e.g. TensorFlow, PyTorch, Scikit-Learn)".