University of Washington

Visual Job Search

Data-Driven Job Search Dashboard

UI/UX Design


Visual Job Search helps job seekers curate and apply to high quality opportunities through interactive visualizations of employee ratings such as company culture, pay and benefits, and work/life balance.

Over the course of 10 weeks, my team delivered research insights, interactive prototypes, a dataset of ~82,000 job postings and company ratings, and a live job search tool.


While existing job search tools are great for finding available jobs, they do little to assist in evaluating opportunities by keeping company reviews and job postings separate or minimally integrated. The wealth of employer ratings and reviews online is helpful but combing through to gather insights is a time-consuming process.

indeed job search
Job seekers often have to search multiple sites to get a complete understanding of the available opportunities

Since the human brain is much more adept at interpreting and drawing insights from visual information than text or numbers, my team set out to create a tool that would assist job seekers in evaluating potential employers through the use of data visualizations.


User Research

Survey and Interviews

We conducted 6 hours of interviews and surveyed 22 people who had recently landed a job or were actively looking for work to learn about their job search strategies and decision factors.

As expected, all of our interviewees and survey respondents had searched for work and looked at company reviews online.

Job search factors varied widely among our participants, and included considerations such as pay and benefits, company structure, work style, company culture/image, leadership, team size, and diversity.

vjs survey result
Each participant seemed to have a slightly different combination of factors that they cared about most.


Based on our survey and interviews, we created a representative persona, Tania. Keeping Tania’s in mind helped ground our design and reminded us to design in a way that was flexible enough to support job seekers with different priorities.

vjs persona

Gathering a Dataset

Employer Reviews

To build out an initial dataset, I wrote up some Python code to request data from the Glassdoor Companies API. After cleaning this dataset I was left with 29 data points for each of 9,394 companies, including ratings of various aspects of each company (i.e. compensation, work/life balance, culture and values), company reviews by former employees, information about the CEO and senior leadership, and the industry segment. We used this dataset for our initial prototype.

Job Postings

Glassdoor does not make job postings available through its public API, so for the second stage of data collection I used company names from the initial dataset to query Indeed’s Web Services API, collecting job title, posting URL, city and state, and geolocation data.

After hitting the API request limit and merging and cleaning the two datasets, I arrived at an 85 MB CSV file consisting of 35 columns, with roughly 83,000 unique job postings across 4,200 companies, at 98% overall completion. This dataset was used from our second prototype onwards.

dataset cleaning
I used Trifacta to validate fields and filter out bad data.



We kicked off our design process by sketching out some possibilities for different ways to visualize and search through the reviews dataset we’d gathered.

vjs brainstorm sketch
Early concept prototype

Initial Prototype

Analyzing the initial dataset, we realized there was a potential to create some meaningful visualizations by aggregating metrics related to specific themes.

Based on the priorities expressed by our research participants, we created a “career related’ visualization and a ‘culture related’ visualization.

v1 culture vis
Culture visualization (first tab)
v1 career vis
Career visualization (second tab)

Usability Testing

Our five participants immediately understood that companies in the upper-right quadrant of each scatter plot had the best ratings:

“the same companies are coming in low… don’t work at Diane’s swimwear!”

- usability participant

Despite the ease of interpreting the position of each data point, participants had difficulty gleaning insights from the color and size encodings.

vjs usability

Rapid Iteration and Testing

Taking the lessons from our first prototype we began rapid iterative testing and evaluation (RITE), producing and testing six prototypes within a week.

Incorporating the updated dataset with live jobs data during this stage enabled us to develop and refine different workflows for job search and filtering, customizable linked dynamic visualizations, a color scheme for effective data interpretation, and supporting copy for the interface. We got pretty good at using Tableau too.

v6 prototype
Prototype with customizable criteria
v9 prototype
Prototype with customizable criteria, filterable fields


Tableau Prototype

Backed by an extensive database of job postings and company ratings, our final Tableau prototype used data visualization best practices to enable job seekers to uncover insights and evaluate job opportunities before they apply.

final prototype filled
View the Final Prototype

Data Overview

A customizable scatter plot shows an overview of the distribution of relevant opportunities based on search terms and user-selected rating dimensions.

Search and Filter

Users can narrow down results by company, job title, industry, and location using the filters at the top of the application. By interacting with the data visualizations and sliders users can filter by ratings such as company culture, pay and benefits, and work/life balance from actual employees.

Details on Demand

A customizable parallel coordinates plot shows ratings across all metrics for top companies. Users can also hover or click on a specific company to see the associated ratings values and job title.

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