Project Description

In a nutshell

The Business Intelligence Analytics & Change team provides trusted support and insight across the divisions and leadership in Sainsbury’s to help them make effective decisions. The role of the team is to act as a strategic partner to all business divisions by providing analytics that inform real improvements and actions across the business through insight at an ad-hoc, operational and strategic level.

The analytics analyst for the Business Intelligence team will be responsible for the extraction and manipulation of large datasets primarily from the BI Enterprise Data Warehouse, in order to support the business with their questions and insight requests. They will apply statistical methods to the investigation and implementation of projects where required.

What I need to do

  • Partner with business users to understand analytical / data gaps and assist them with the formulation of questions to answer challenges/ issues they are facing
  • Assist the business with their questions by building custom scripts analysis, analytics and insight as needed (using tools such as SAS, R, Python, Tableau, MS-SQL, D3)
  • Provide statistical analysis, build analytical models, handle data and reporting, deliver visualizations in the form of interactive dashboards
  • Ensure that each project and activity drives a business benefit
  • Respond to requests in a timely manner.
  • Ensure the analytics libraries (Code, deliverables/ methodology) are well organised and data dictionaries are kept up to date
  • Work on multiple projects relating to Business Intelligence and Analytics, working both cross functionally and independently
  • Contribute to the team’s wider vision, Analytics strategy and ways of working – such as hackathons, trying out new tools and approaches

Example outputs include

  • Reusable models
  • Statistical analysis
  • Presentation and summaries of findings

How I will succeed

  • Delivery of high-quality, objective analysis and insight
  • Strong organisation skills and the ability to decide the appropriate level at which analysis needs to be carried out
  • Analysis and outputs agreed clearly and delivered according to managed expectations

What I need to know

  • High calibre graduates (2.1 or above, BSc or MSc level) in computing or another technical qualification. A-level Mathematics is essential.
  • Exposure on various analytical techniques including time series analysis, cluster analysis, regression analysis, predictive modeling techniques, decision trees
  • Statistical understanding including: time series analysis, dynamic optimisation, operational research, decision / game theory, multi-variant non-linear regression, dynamic programming, numerical analysis
  • Experience in data extraction, manipulation and automating processes – using open source statistical software and analysing data in a corporate environment.
  • Strong knowledge of SQL particularly within SAS and Oracle / Teradata environments and excellent skills in Excel.
  • Use of Business Intelligence (BI) tools, particularly for visualisation and interpretation of the results would be beneficial eg SAS, SPSS, MS-Excel, MS-SQL, Tableau, Hive, R
  • Good communicator with the ability to understand requirements and present data back to stakeholders in a clear and positive manner
  • Ability to work to strict timescales and juggle multiple requests

What I need to show

  • Ability to relate data / analytics to real-world business
  • Logical approach to problem solving, with attention to detail and good communication skills
  • Able to explain technical concepts in simple terms to a non-technical audience
  • Confident in questioning the validity of information and in offering ideas and solutions
  • Self-starter, strong initiative and commitment
  • Knowledge of, or an interest in, Multi-Channel retailing

Resources available to me

  • Software and Hardware as required
  • No direct reports
  • Advice and support from team and manager

What decisions I can make

  • Most appropriate way to approach key questions and how to resolve data, analytical and statistical issues.
  • Best ways to present data to end users and disseminate best practice for data extraction and modelling across the Change and Analytics team.