Data Science demand is best read through vacancy evidence, role context, and the technologies used alongside it. ApplyDjinn separates core and required usage from supporting mentions so candidates can distinguish hands-on ownership from general stack exposure. Current hiring evidence includes Google, DoorDash, Pinterest. Leading observed locations include United States, New York, United States, New York, New York, United States.
106 live roles22 hiring companies4% remote-friendly
Vacancy evidence map
Where Data Science carries hiring weight
Each segment represents roles where Data Science was parsed at that evidence tier. This keeps a required production skill separate from contextual stack language.
9 Core28 Required57 Supporting12 Optional
37core + required35% of mentions
4.8roles per company22 employers
Market interpretation
What Data Science demand means in practice
Data Science can represent different work depending on the role family and delivery environment. The live evidence map on this page shows whether employers treat it as a primary capability, a required implementation skill, supporting context, or an optional advantage. Companion demand such as AI, SQL, Machine Learning, Python helps explain the actual systems and responsibilities behind the keyword.
A credible Data Science application should connect the skill to a concrete system, decision, and result. State what you built, configured, migrated, secured, tested, or operated; name the scale and constraints; and explain the outcome. This gives hiring teams stronger evidence than a standalone skill badge or an undifferentiated list of tools.
Data and AIApplied engineeringPlatform deliveryAnalytics and operations
Reading the market
Signals to look for
Read Data Science together with AI, SQL, Machine Learning, Python; combinations reveal the likely workload and ownership boundary.
Core and required mentions deserve more weight than incidental text in a company or product description.
Role title, seniority, location, and adjacent skills determine whether prior experience transfers directly or needs a focused ramp-up.
It counts fresh vacancies where Data Science was found in title, requirements, responsibilities, or stack evidence. The tier breakdown separates central requirements from supporting and optional mentions.
How should Data Science appear on a CV?
Tie it to a specific project or production responsibility, the surrounding stack, the constraint you handled, and an outcome. Match the depth of the claim to the evidence shown in the target vacancy.
How Data Science appears in job requirements
Inferred from the title and requirement wording in 106 active roles; each role is counted once. Read the methodology.
Posting evidence, not a company-wide stack claim
Main / core
8%(9)
Required
26%(28)
Supporting / context
54%(57)
Nice-to-have
11%(12)
Skills mentioned with Data Science
A shared posting does not always require both skills. Explicit alternatives are counted separately.
AI
SQL
Machine Learning
Python
Scalability
Statistics