June 1, 2023
We leveraged Job analytics to look at the hiring landscape of one of the most heated industries of the moment: chip making.
The US semiconductors and chip making industry has become strategically important due to vulnerabilities in global supply chains revealed by the pandemic as well as to the increased reliance on technology and the recent AI boom. Nvidia, the leading chip maker in the US, recently achieved a temporary valuation of $1 trillion , as more and more companies are integrating generative artificial intelligence tools into their products.
The government acknowledges the crucial role of the industry in propelling technological progress and safeguarding national security. To ensure stability and independence, the administration is actively investing in semiconductor manufacturing while prioritizing efforts to attract and retain strategic talent within the field.
RavenPack Job Analytics is an insightful, predictive and actionable dataset sourced by LinkUp directly from the websites of more than 60,000 employers globally. It covers more than 200 million job postings, 5,000 roles and 3,000 skills, qualifications and benefits
To see how these developments are being reflected in the job market, we explored the hiring landscape for semiconductor engineering using Job Analytics and the RavenPack Python library, from the companies actively recruiting to the roles and skills in high demand.
RavenPack Job Analytics is an insightful, predictive and actionable dataset sourced by LinkUp directly from the websites of more than 60,000 employers globally. It covers more than 200 million job postings, 5,000 roles and 3,000 skills, qualifications and benefits.
First, it is possible to identify the key players by running a keyword search across all positions using “ semiconductor engineering ”:
Note that you can easily refine or extend the search directly in the Python notebook.
'search = '" semiconductor engineering " or " computer chip "'
Here is what that search returns:
The search was performed against jobs created, but it is possible to look only for active jobs. Additionally, you can very simply restrict the search to a country, like the US:
us_country=' 3D4567 ' Filter_columns = { " location_country ": us_country }
Here, we use the location “ United States ” from the RavenPack Knowledge Graph, a point-in-time mapping of companies, people, places, concepts and more covering 12 million entities. ‘ 3D44567 ’ is the entity ID for the United States as a location.
Having said that, this search would return foreign companies recruiting in the US, since it is the location of the job opening that is examined, so instead, if you want to see only US companies, you can filter based on the country of the company itself:
Filter_columns = {" company_country_code ": ' US '}
Note how the Knowledge Graph enables you to join the dots between a job, the company hiring, and details of that company.
It can be useful to refine the search by position types. Using the RavenPack Indicators library, the ranking of the top 10 positions in that query becomes:
# Get indicator query
query_posi = indicator.get_indicator_query(indicator_params, output_params)
# Compute top-10 companies from the indicator query
top_query_posi = f "select position, position_name, sum(val) as total_jobs \
from ( {query_posi} ) \
where position != 'empty' \
group by 1, 2 \
order by 3 desc \
limit 10"
top_df_posi = SnowflakeConnection().execute_query(top_query_posi)
top_df_posi
Which returns:
But that’s not all. If you want to explore whether that industry has enough potential workers matching the required skills, you need to identify the most common skills in demand for these positions. Because RavenPack identifies these skills as part of a competency taxonomy, a few lines of Python can deliver the data:
query_skills = indicator.get_indicator_query(indicator_params, output_params)
# Compute top-30 companies from the indicator query
top_query_skills = f "select taxonomy_ent, taxonomy_ent_name, sum(val) as total_jobs \
from ( {query_skills} ) \
limit 30"
top_df_skills = SnowflakeConnection().execute_query(top_query_skills)
top_query_skills
As the demand for semiconductor engineers continues to rise, understanding the hiring landscape and the skills in demand becomes crucial. Datasets like RavenPack Job Analytics can provide market leaders and policymakers visibility into trends shaping the workforce and the future of work itself.
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