21 Apr

Consultant Data Science - Hyderabad - Verizon

Consultant Data Science
Hyderabad TS
21 Apr, 2017 7 days ago

Verizon as the company that open the jobs vacancy, have some qualification and spesification especially for the Consultant Data Science jobs vacancy. To find out more information and about qualification and spesification details, walkin interview schedule, the address of the company, the company contact info (email/phone number) of Verizon company, please start to apply for the job vacancy with fill the jobs application with click the 'Apply This Job' button below.


  • Collaborate with business and IT teams to understand the challenges and deliver business insights and reporting solutions.
  • Should have hands on experience in spend analytics pertaining to Procurement and Supply Chain
  • Adept at working with large data sets.
  • Integration of data from different sources such as spend database, ERP systems etc
  • Ability to build relationship models for dashboards and reports using large data.
  • Establish scalable, effective, automated processes for dashboards, data validation, model implementation and large scale data analysis.
  • Strong fundamentals in graphic design concepts to generate visually effective reports.
  • Stay well-informed of latest developments in the analytical space across industries for potential applications.
Tool Exposure and Skill set required
  • Proficient in Microsoft Excel including Power Pivot, Power Query, Power BI and Power Pivot.
  • Well experienced in SQL basic and VB Macros.
  • Exposure to at least one statistical analysis tool such as R or SAS.
  • Exposure to other BI and reporting tools such as Tableau.
Other Desired Qualifications
  • Past experience in telecom industry or handling such clients is an added advantage.
  • Subject Matter Expert in sourcing and procurement.
  • Preferably six sigma green belt certified.
Preferably Masters degree in Statistics
Bachelors degree in Statistics
Extensive client facing experience of 6-7 yrs. with hands on experience in data Modelling

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