Introduction — the rise of a career-defining role
In 2019, Glassdoor crowned Data Scientist as the top U.S. job. That wasn’t a fluke or a popularity contest — it reflected a unique convergence: explosive demand for analytics skills, strong salaries, plentiful job openings, and high employee satisfaction. The title captured the zeitgeist of a business world shifting from intuition-driven decisions to evidence-driven strategy.
This article breaks down exactly why Data Scientist topped the list, what structural forces made it so valuable, and what lessons both employers and job seekers could draw from the era when data science became shorthand for modern competitive advantage.
What Glassdoor measures (and why it matters)
Glassdoor’s “Top Jobs” ranking typically factors in several measurable variables:
- Median base salary — how well the job pays.
- Number of job openings — market demand.
- Job satisfaction — company ratings and employee sentiment.
- Job market competitiveness — supply vs. demand signals.
A role that scores highly across these dimensions becomes a strong candidate for the top spot. In 2019, Data Scientist checked all these boxes: generous pay, skyrocketing openings, positive worker reviews, and a skills shortage that kept supply tight.
1) High pay — compensation that reflected scarce skills
Data science salaries in 2019 were notably high across the U.S., especially in tech hubs, finance, and top-tier consultancies. The premium came from two factors:
- Specialized technical skills (statistics, machine learning, data engineering, programming in Python/R, SQL) that few professionals had at scale.
- Direct business impact — well-built models could reduce costs, increase revenue, and unlock new product lines.
For employers, paying a premium for someone who could translate messy data into actionable models was an investment with clear ROI. For talent, the compensation packages (often augmented with stock or bonuses in tech firms) made data science an attractive career path.
2) Strong demand — data was suddenly everyone’s priority
By 2019, companies across industries were racing to become “data-driven.” This meant:
- Startups building analytics-first products.
- Traditional firms modernizing operations with predictive analytics.
- Governments and non-profits investing in data teams for policy and impact analysis.
Job boards and labor-market analyses showed a tremendous uptick in postings for data-related roles. Crucially, demand wasn’t restricted to Silicon Valley — healthcare, retail, manufacturing, and finance all needed data-savvy professionals. That breadth of demand magnified the role’s value and job security.
3) High job satisfaction — interesting work + visible impact
Glassdoor includes employee sentiment in its rankings. Data scientists often reported:
- Intellectually stimulating problems — working on messy, real-world questions.
- Autonomy and creative problem solving — designing experiments and models.
- Visible impact — analytics projects often have measurable outcomes (e.g., conversion lift, cost savings).
This combination — stimulating technical work with real business effect — fuels high job satisfaction. Where roles feel meaningful and rewarding, retention improves and employer reviews reflect that positivity.
4) Talent shortage — supply couldn’t keep up with demand
A key reason salaries stayed high and job openings remained abundant was a talent shortage. The pipeline of PhDs, master’s graduates, and bootcamp-trained professionals was growing, but not quickly enough to match corporate demand. Reasons included:
- High technical entry barriers — mastering statistical thinking and software engineering simultaneously is hard.
- Rapidly evolving toolset — companies wanted people who could use modern ML frameworks, cloud platforms, and productionized pipelines — not just prototyping notebooks.
- Experience premium — employers often preferred candidates with demonstrated business impact, not just theoretical knowledge.
This supply gap created bargaining power for candidates and a war for talent among employers.
5) Role breadth and career flexibility
In 2019, “Data Scientist” was an umbrella title that covered many valuable skills:
- Exploratory data analysis and visualization.
- Statistical modeling and hypothesis testing.
- Machine learning (regression, classification, recommendation systems).
- Basic data engineering (ETL, SQL).
- Domain expertise and storytelling with data.
This mix made data scientists versatile and able to move across domains. The role also functioned as a gateway to senior analytics positions, ML engineering, product analytics, and leadership roles — increasing its perceived long-term value.
6) Education and reskilling boom — more routes into the field
While supply lagged demand, 2019 saw more accessible education pathways:
- University programs and specialized master’s degrees in data science.
- Online courses and nanodegrees.
- Intensive coding and data-bootcamps.
These options democratized entry to some extent, but the market still favored candidates who combined technical depth with business savvy. Employers valued demonstrable projects and portfolios — not just certificates — keeping the credential bar relatively high.
7) Institutional adoption — organizations changed processes
By 2019, the role of data science shifted from isolated proof-of-concept projects to embedded, productizing functions:
- Companies invested in data platforms and data engineering to operationalize models.
- Cross-functional teams began to rely on analytics for decision making.
- KPIs and OKRs were increasingly tied to data insights.
As organizations internalized analytics, the need for data scientists became structural rather than temporary — raising the long-term job outlook for practitioners.
8) Media and cultural factors — hype and signaling
The broader hype around AI and machine learning also helped. Headlines touting “AI-driven” success stories amplified demand and shaped perception. For many firms, hiring data scientists became both a strategic necessity and a signal to investors and competitors that they were serious about innovation.
This signaling loop could exaggerate demand in some cases, but it also helped attract more talent and investment to the field.
9) Limitations and growing pains
Being #1 didn’t mean the job was perfect. Some structural issues surfaced:
- Role ambiguity — mismatch between business expectations and technical realities (deployable models vs. exploratory analysis).
- Siloed teams — analytic work sometimes isolated from production engineering.
- Burnout — heavy expectations and pressure to deliver measurable wins quickly.
These challenges led companies to refine job descriptions, split roles (e.g., data engineer vs. ML engineer vs. business analyst), and invest in better onboarding and tooling.
What Glassdoor’s #1 ranking meant for job seekers & employers
For job seekers:
- Data science was a lucrative and intellectually rewarding path — but success required a mix of technical skill, domain knowledge, and communication ability.
- Building a portfolio of real projects, contributing to open-source, or gaining domain experience became crucial differentiators.
For employers:
- Attracting top talent meant offering not just salary, but interesting problems, career development, and a path to productionalize work.
- Investment in infrastructure (data platforms, MLOps) and clear role definitions was necessary to avoid frustration and churn.
Looking back — legacy of 2019’s data-science peak
Glassdoor’s 2019 ranking captured a distinct moment: the transition from “data as a novelty” to “data as core business fabric.” The role’s top ranking helped formalize career paths, broaden educational offerings, and accelerate enterprise investment in data capabilities.
Over time, the field matured: job titles proliferated, tooling improved, and organizations got smarter about dividing responsibilities. But the 2019 spotlight helped set that professional ecosystem in motion.
Quick tips for aspiring data scientists (actionable takeaway)
- Build demonstrable projects. A handful of well-documented, end-to-end projects beats a long list of courses.
- Learn production skills. Basics of SQL, version control, and deploying simple models on cloud platforms matter.
- Develop storytelling skills. Translating technical results into business action is what separates analysts from data scientists.
- Choose a domain. Industry knowledge (healthcare, finance, retail) amplifies technical skills.
- Network and open-source. Contributing code or joining data competitions (Kaggle, etc.) increases visibility.
Conclusion — more than hype: a structural shift
Glassdoor’s 2019 choice of Data Scientist as the top U.S. job reflected more than a hiring fad. It marked an inflection point in how organizations value data-driven decision making. High pay, solid demand, satisfying work, and a genuine skills shortage combined to make the role especially attractive. For employers and job-seekers alike, the ranking served as a wake-up call: data capabilities were now central to strategy — and those who invested early gained an edge.
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