Standard Chartered Hiring Data Scientist

Location: Chennai
Experience: 4+ Years
Skill: Knowledge for Data Scientist

Job description

KEY RESPONSIBILITIES:

A Data Analyst interprets data and helps turn it into information that enables or improves a business process, thus affecting business decisions. Data Analysts gather information from various sources and interpret patterns and trends to make it digestible for others. They must have strong analytical skills, but above all have a burning curiosity to understand, and make sense of, data.

They will work closely with:

  • Data Managers, who define the team’s business requirements, objectives, strategy and priorities
  • Data Engineers, who design and implement the processes by which data is collected and generated
  • Data Quality Analysts, who define data quality control requirements and oversee these on a day to day basis to ensure constant system health
  • Upstream data teams, who provide the data that the analyst is sourcing
  • Downstream Process Owners, who depend on the data to perform their business function

Responsibilities:

  • Define clear, concise and detailed business requirements for FCC Data that clearly document the data elements and formats that are needed, outline detailed transformation expectations and list the critical data elements that will enable downstream processes to operate effectively
  • Create and maintain documentation that articulates the process by which data is extracted, transformed and loaded in FCC that can be shared and understood by others
  • Work with downstream FCC business process owners to constantly improve, refine and expand the datasets to improve the quality and effectiveness of those processes, as well as help them to make sense of the data, providing training where required, and derive meaningful BI / MI
  • Conduct detailed analysis of upstream changes that impact FCC data – for example the introduction of a new products – to ensure that requirements remain up to date and define any new ones as necessary
  • Identify areas of overlap or data gaps that can lead to increased value, either by eliminating redundant processes or expanding existing data models
  • Produce accurate and insightful dashboards and reports detailing the health, content and insights available from the data, making that actionable for stakeholders and meaningful for management decision making

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