Data Science Analyst

If you join Accenture, you can make great ideas happen for some of the world’s most dynamic companies. With broad global resources and deep technical know-how, we collaborate with clients to cultivate ideas and deliver results. Choose a career at Accenture and enjoy an innovative environment where challenging and interesting work is part of daily life.

As part of Digital practice, you will manage, architect and analyze big data in order to build data driven business insights and high impact data models to generate significant business value. Digital covers a range of industries creates models and processes to collect, distill and interpret data with a view to aid better, more informed decision making. Examines and explores data from multiple disparate sources with the goal of discovering insights which in turn can provide competitive advantage for our clients.

Data Science Analyst

Key responsibilities may include:

• Consulting with clients and deciding what data are needed to answer specific questions or problems related to business
• Collects, cleans and aggregates data in order to use them for Data Analysis
• Collaborates with team members towards achieving clients’ objectives
• Responsible for implementing statistical methodologies and data analysis techniques as set forth in project requirements
• Generates tables and figures for presentation and interpretation of results in accordance with project requirements
• Validate data analysis results with team members to ensure high quality deliverables
• Assists in development of documents or presentations as needed
• Identify, interpret and communicate meaningful findings to key individuals (i.e. answer the “so what” questions)
• Participate in internal research and development activities as needed

Professional Skill Requirements:

• BSc and MSc or Phd in Statistics, Mathematics, Operational Research, Econometrics or related science field from a well-established University
• Knowledge of advanced data analysis techniques that include customer segmentation, propensity modelling, optimization techniques and predictive modeling
• Proficiency in using SQL and R language to manipulate datasets and perform data analysis
• Familiarity in SAS, SPSS; Proficiency in any of those will be considered as a strong asset but it is not a prerequisite

Source: www.kariera.gr