March 22, 2024
Peter Hafez, our Chief Data Scientist, explores how RavenPack's Custom Factors Library simplifies extracting actionable insights from job postings, using Generative AI skills as a practical example.
Peter Hafez
Chief Data Scientist
RavenPack
Alternative data used to be the exclusive domain of quantitative investors, granting them a significant alpha advantage through their investments in research and infrastructure. Today, the market recognizes that traditional data (market & fundamental) is insufficient for competitive edge. As a result, the ability to efficiently consume vast amounts of data is rapidly becoming the industry standard.
The recently launched RavenPack Factors provides investors with ready-made indicators that analyze news, filings, and more to gauge market sentiment and identify potential movers. It goes beyond pre-built options with a custom library, which lets users explore vast datasets and uncover hidden trends through their own factor creation.
Let's see how this works in practice.
A recent report by the Royal Bank of Canada, titled "RBC Elements: Software Job Postings Deep Dive," showcased the extraction of insights about Gen AI from the job market by analyzing thousands of job postings. We replicated this study using our Custom Factors Library, achieving comparable results with just a few clicks.
Here's a glimpse into the process:
We begin by selecting the parameters we wish to analyze, including companies, locations, and skills. For instance, we aim to identify the top companies in the US actively seeking candidates with Generative Artificial Intelligence skills in 2024. Utilizing our extensive knowledge graph, we identify relevant Gen AI skills:
select * from RAVENPACK.KNOWLEDGE_GRAPH.JOBS_TAXONOMY_1_1 where entity_name LIKE'%Generative%'
From the results, we select the skill "Generative Artificial Intelligence - 38803F" for this example.
Next, utilizing the factor library, we generate a job factor comprising the count of jobs created by companies with the country code US and the skill "Generative Artificial Intelligence - 38803F" listed in their job descriptions. Setting the aggregation by company, the library retrieved daily factors for each entity. To obtain the year-to-date count, we perform an additional aggregation.
# Parameters to define the factor
factory = IndicatorsFactory() indicator = factory.create_indicator("jobs_v2") params = indicator.get_parameter()
# Define the indicator parameters
params.set_indicator_params(
indicators=["jobs_created"], filter_columns={"company_country_code": ['US'], "taxonomy_ent" : ['38803F']}, aggregation_columns=["company"], start_date="2024-01-01", end_date="2024-02-27"
)
# Retrieve Data
created_df = indicator.get_indicator(params)
# Aggregate jobs created in full period and order
aggregated_df = created_df.groupby(['RP_COMPANY_ID', 'RP_COMPANY_NAME'])['FACTOR_SCORE'].agg('sum').reset_index() aggregated_df = aggregated_df.sort_values(by='FACTOR_SCORE', ascending=False)
# Display top 10 results
aggregated_df.head(10)
Within seconds, the results are available, allowing us to easily identify the top 10 companies hiring for Gen AI in the US.
For instance, KPMG emerges as a top contender, as evidenced by a sample job listing provided below:
Known for being a great place to work and build a career, KPMG provides audit, tax and advisory services for organizations in today's most important industries. Our growth is driven by delivering real results for our clients. It's also enabled by our culture, which encourages individual development, embraces an inclusive environment, rewards innovative excellence and supports our communities. With qualities like those, it's no wonder we're consistently ranked among the best companies to work for by Fortune Magazine, Consulting Magazine, Working Mother Magazine, Diversity Inc. and others. If you're as passionate about your future as we are, join our team.
KPMG is currently seeking a Product Manager - Generative Al Applications to join our National Audit Technology organization.
By making minor adjustments to the factor library, we uncover additional insights. For example, we explore sectors exhibiting heightened demand for Gen AI skills and identify specific job roles associated with this expertise.
Our data aggregation by sector reveals that Gen AI skills are no longer confined to the Technology sector but expand into other industries.
Define the indicator parameters
indicators=["jobs_created"], filter_columns={"company_country_code": ['US'], "taxonomy_ent" : ['38803F']}, aggregation_columns=["sector"], start_date="2024-01-01", end_date="2024-02-27"
Moreover, a simple query reveals that Gen AI skills are sought after not only by software engineers and data scientists but also by professionals in roles such as product managers and marketers.
indicators=["jobs_created"], filter_columns={"company_country_code": ['US'], "taxonomy_ent" : ['38803F']}, aggregation_columns=["position"], start_date="2024-01-01", end_date="2024-02-27"
This example shows the ease of extracting valuable insights from job postings. Leveraging our Custom Factors Library, which operates atop news content, offers additional insights. For instance, it allows investors to compare companies discussing Gen AI in their press releases, earnings conference calls, etc., with those substantiating their commitment through hiring activities. This capability empowers us to discern companies genuinely invested in Gen AI from those engaging in mere media theatrics.
To make advanced insights more accessible, RavenPack has analyzed terabytes of news articles, filings, and transcripts, to produce actionable sentiment and market-moving factors that work for every investor — from fundamental to discretionary trading. Explore the Factors library here.
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