Offer summary

Qualifications:

Proficiency in SQL for database management and data analysis., Experience with programming languages such as Python or R for statistical analysis., Strong skills in data visualization tools like Tableau, Jupyter Notebook, and Excel., Solid understanding of statistics and mathematics to interpret data accurately..

Key responsibilities:

Gather and clean data from various sources, including surveys and observational studies.

Interpret data to identify patterns and trends relevant to workplace and real estate decisions.

Present findings through visualizations, reports, and presentations to stakeholders.

Consolidate and categorize data to create structured databases for analysis.

Job description

Key Roles and Responsibilities:

A Workplace Data Analyst gathers, cleans and interprets data sets to extract insights from the data and help inform workplace and real estate decisions
To be able to support a descriptive analysis telling us what happened, diagnostic analysis telling us why it happened, predictive analytics which forms projections about the future, and prescriptive analysis which creates actionable advice on what actions to take relation to a client's workplace and real estate.
Gathering data: This could include developing and conducting surveys, based upon Haworth's existing cultural competing values framework. As well as modelling workplace surveys to measure workplace activities, satisfaction, and stress, as examples. Gather data on space utilization using turnstile badge data, booking applications, sensors, and observational studies.
Clean data: Cleaning the data, removing duplicates, errors, or outliers to maintain the data quality in a spreadsheet or through a programming language so that interpretations won’t be wrong or skewed.
Consolidate, Categorise and Model data: Creating and designing the structures of a database. Choose what types of data to store and collect, establish how data categories are related to each other, and work through how the consolidated data appears.
Interpret data: Interpreting data to find patterns or trends in data that will help answer specific workplace and real estate questions from clients
Present: Communicating the results of your findings will be a key part of your job. You do this by putting together visualizations like charts and graphs, writing reports, and presenting information to interested parties.

Data analyst technical skills:

Must Have‘s:

Database tools: SQL can handle larger sets of data and is widely regarded as a necessity for data analysis.
Programming languages: Statistical programming language like Python or R
Data visualization: Presenting findings in a clear and compelling way is crucial for stakeholders to understand analysis. Tableau, Jupyter Notebook, and Excel are among the many tools used

Good to Have’s:

Power BI
Statistics and Maths: A solid grasp of statistics and maths will help you determine which tools are best to use to solve a particular problem, help you catch errors in your data, and have a better understanding of the results

Data analyst workplace skills:

Problem solving: A good understanding in concept of the workplace question being asked and the problem that needs to be solved. Be able to find patterns or trends that might reveal a story. Having the critical thinking skills will allow you to focus on the right types of data, recognize the most revealing methods of analysis, and catch gaps in the data and analysis.
Communication: Being able to get your ideas across to other people will be crucial to your work. Strong written and speaking skills to communicate with colleagues and other stakeholders.
Industry knowledge: Knowing about the real estate and workplace industry will be an advantage.

Required profile