Skill Demand Index
Years of Data Science Experience — Demand & Depth Analysis
Based on 1 scored job postings out of 3,786 total. Depth levels reflect actual proficiency tiers, not just keyword presence.
0%
Demand Rate
L1
Median Depth
100%
Gap Rate
1
Jobs Analyzed
Minimal
Most employers want Years of Data Science Experience at introductory awareness.
Overview
What is Years of Data Science Experience?
Market context for Years of Data Science Experience in the current job market
Years of Data Science Experience is required in 0% of scored job postings on ShouldApply, making it a growing skill in the current job market. Employers looking for Years of Data Science Experience typically want candidates who can demonstrate real proficiency, not just surface awareness.
What the data shows for Years of Data Science Experience:
- •Required in 0% of all scored postings — demand is growing as more employers add it to requirements
- •Employers typically expect L1 depth — foundational knowledge with practical application
- •Most demand comes from Data Science / ML roles — 100% of all Years of Data Science Experience jobs
What L1 means in practice:
L1 (Minimal) means you can discuss the concept but haven’t used it in production. Many entry-level positions accept this.
This means employers aren't looking for someone who has used Years of Data Science Experience once or twice. They want evidence of professional application — shipped work, measurable outcomes, and the ability to operate independently.
Common skill gaps:
The gap rate of 100% means most applicants lack Years of Data Science Experience at the depth employers need. This is a real opportunity for candidates who invest in building genuine proficiency.
Which roles need Years of Data Science Experience most:
Data Science / ML positions drive 100% of demand. Skills commonly paired with Years of Data Science Experience include SQL and Python.
Depth Level Distribution
Proficiency Distribution
How candidates match Years of Data Science Experience requirements across 1 scored evaluations
Average depth: L1.0·Median depth: L1.0
Salary Correlation
Pay Impact
How Years of Data Science Experience affects compensation based on postings with disclosed salary data
Without Years of Data Science Experience
$139K
Median $130K
979 jobs
Skill Demand Insight
“Years of Data Science Experience appears in 0% of all scored jobs.”
From 1 scored job postings
Skill Pairings
Commonly Paired Skills
Other skills that frequently appear alongside Years of Data Science Experience
Role Breakdown
Top Role Categories
Job categories most likely to require Years of Data Science Experience
Gap Analysis
Gap Rate Explained
How often Years of Data Science Experience is identified as a skill gap (L0–L1) in scored applications
High gap rate — most candidates are underqualified
When Years of Data Science Experience appears in a job's requirements, 100% of scored applicants received an L0 or L1 (missing or minimal).
Frequently Asked Questions
Is Years of Data Science Experience in demand in 2026?
Yes. Years of Data Science Experience appears in 0% of scored job postings on ShouldApply, making it a growing skill in the current market. Based on 1 analyzed jobs, demand is steady across multiple role types.
What level of Years of Data Science Experience do most jobs require?
The median required depth is L1. Many positions accept basic to intermediate proficiency.
Does knowing Years of Data Science Experience increase salary?
Salary data for Years of Data Science Experience is still accumulating.
What other skills pair with Years of Data Science Experience?
The most common pairings are SQL, Python, GitHub, Analytical Methodologies Design, Big Data Tools. Strengthening these alongside Years of Data Science Experience improves your fit across more positions.
What roles need Years of Data Science Experience the most?
Top roles: Data Science / ML. Data Science / ML positions have the highest demand at 100% of all Years of Data Science Experience jobs.
How do I improve my Years of Data Science Experience level?
L1→L2: online courses and personal projects. L2→L3: daily professional use and shipped work. L3→L4: mentoring others and optimizing processes. L4→L5: architecture decisions, open source contributions, or published work.
See how you stack up against Years of Data Science Experience job requirements
ShouldApply scores your profile against each skill at the depth level jobs actually need.
Analyze my Years of Data Science Experience gaps →See how your depth compares to what employers actually require
All Skills · Roles · Companies · Browse Jobs