JobsPikr Product Update: New And Exciting Features Added
The job market has never seen a dull day. With the world adapting to the new normal, we’ve seen the jobs’ demand and supply gap widen even further. Today, enterprises are looking at elegant solutions to help them bridge this gap. Gone are the days, when users can afford to lose time on vetting the scrapped job boards. The need of the hour is quality and exclusive job data, and the need is now! Let us take a look at the new JobsPikr Product Update that we are about to roll out this month.
So now it is, as JobsPikr launches a series of exciting new features to help you gain access to and make a better sense of the job data. Here’s how.
Unveiling JobsPikr Product Update
Fortune 500 Dataset
Our amazing set of users have always asked if they could receive data directly from career pages of companies, apart from popular job boards of course. We have heard from you. From now on, you’d be able to subscribe to job data feed directly from Fortune 500 companies’ career pages! This is a huge leap, given the technical complexities involved in setting up this data pipeline and its ongoing maintenance. It sets up JobsPikr as the unique and go-to-solution for exclusive job datasets of incredible value.
With this JobsPikr Product Update, this dataset is available at the top of the line Enterprise Plan. Existing customers may contact their respective Sales / Account Managers or Customer Support to gain access. We do have some early bird discounts going on as well.
Remote Work Flag
With a lot of jobs going remote, we recently launched a new field in our data, viz., is_remote. Our automated tagging system reads through the job posting content and determines if a given job is remote in nature or not. This is a Boolean field that would have True or False values.
So no more wastage of your precious time, in determining the location for ‘Remote’ and ‘Gig jobs’.
Salary Inference
Until now, we provide salary information as a text field – is available from the source websites We realized the limitation with this approach and upgraded it. Now onwards, we will also be delivering salary inferences split across four separate, query-able fields: inferred_salary_currency, inferred_salary_from, inferred_salary_to, inferred_salary_time_unit.
This unlocks a world of new possibilities in terms of analyzing salary trends and even querying for jobs based on a particular salary range.
As of now, we are only handling cases where salary information provided as a separate field from the source websites we collect data from. The model will gradually extend to infer and predict salaries based on job description text and various other related criteria.
Multi-Value Fields
Some of the fields we provide like location (i.e. city, state, and country) and email address tend to have multiple values for a given record. We realized it interferes not just with our location inferences (i.e. inferred_city, inferred_state, and inferred_country), but also with downloading and importing data.
Hereon, these fields provided as multi-value fields. In other words, these fields will be an array if you’re downloading data in JSON format, and as nested fields if consuming data in XML format. For CSV, it will continue to be pipe-separated values. Our job scraping services is sure to help you stay ahead of the competitive job market.
