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Data Scientist

 Delhi  8+  DS
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Job Details

Company Global Retail & Commercial Bank
Position Data Scientist
Experience 8+
CTC Best in the industry
Location Delhi
Job Code DS
Application Deadline 30 Nov 2020

Skills


As a Data Scientist, you’ll be supporting and collaborating with multi-disciplinary teams of data engineers and analysts on a wide range of business problems including the prevention of financial crime, understanding customer interactions with the bank and the management of credit risk.

You’ll also be:

  • Communicating effectively across the functions or franchises to make business recommendations, gaining business buy-in to solutions tailored to customers need
  • Identifying and working with data sets from across the bank’s data estate to solve difficult, non-routine analysis problems
  • Applying advanced analytical methods as needed, typically as part of a team to support wider decision making
  • Developing and deepening your knowledge of data structures and metrics, advocating for changes where needed for product development
  • Leading analysis activity that includes data gathering and requirements specification in collaboration with business stakeholders

Description


To succeed in this role, you’ll need experience with statistical software, database languages, big data technologies and cloud environments, along with experience articulating and translating business questions and using statistical techniques.

You’ll also hold an undergraduate or master’s degree in a quantitative discipline or equivalent practical experience, as well as evidence of previous project implementation and work experience gained in data analysis related field as part of a multidisciplinary team.

Additionally, you’ll demonstrate:

  • The ability to demonstrate leadership, self-direction and willingness to both teach others and learn new techniques
  • Effective written and verbal communication skills, and the ability to adapt the communication style to a specific audience
  • Expertise with statistical data analysis such as linear models, multivariate analysis, stochastic models and sampling methods
  • Applied experience in deploying machine learning models into a production environment

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