Introduction
In early 2019, Glassdoor released its highly anticipated annual ranking of the best jobs in America. Topping the list was “Data Scientist,” a role that had steadily climbed in visibility throughout the 2010s. The announcement didn’t come as a surprise to industry insiders, but it did spark a wave of curiosity among students, job seekers, and even seasoned professionals who wondered why this role had suddenly become the “dream career” of the modern workforce.
What made “Data Scientist” so special in 2019? Why did it rank above doctors, software engineers, and financial analysts — professions long considered prestigious and lucrative? And most importantly, what lessons can we take from this ranking as the job market continues to evolve in today’s data-driven world?
This article dives deep into why Data Scientist was crowned #1 in 2019 and why it has remained one of the most aspirational roles ever since. We’ll explore the job market dynamics of that time, the skillsets required, salary trends, industry adoption, and the broader implications for the future of work.
By the end, you’ll not only understand why Glassdoor gave the crown to Data Scientist but also gain insights into whether this career is right for you — in 2025 and beyond.
Understanding Glassdoor’s Job Rankings
Before analyzing why “Data Scientist” ranked so highly, it’s important to understand how Glassdoor creates its annual list of top jobs. Unlike lists that only consider salary, Glassdoor takes a more holistic approach, using three core factors:
- Earning Potential (Salary)
- Based on median annual base salary reported by employees.
- Higher salaries increase a job’s overall score.
- Job Satisfaction
- Reflects how employees rate their happiness and fulfillment.
- Scores come directly from anonymous employee reviews.
- Number of Job Openings
- Measures demand for the role in the labor market.
- A job may pay well, but if openings are scarce, it will rank lower.
In 2019, Data Scientist excelled in all three categories. It had a high median salary, exceptional employee satisfaction ratings, and strong job demand across industries. This powerful combination propelled it to the #1 spot.
Why Did Data Scientist Rise to #1 in 2019?
Several unique factors converged in the late 2010s to create the perfect environment for data science to rise. Let’s break down the most influential reasons.
1. The Explosion of Big Data
By 2019, nearly every industry was grappling with an unprecedented surge in data. Companies were collecting vast amounts of information from customer transactions, social media interactions, sensors, smartphones, and more.
However, raw data alone is meaningless. Businesses needed experts who could clean, organize, and analyze this information to make smarter decisions. Data Scientists filled this crucial role, bridging the gap between messy datasets and actionable business insights.
2. Cross-Industry Demand
Unlike specialized careers tied to a single sector, data science had applications in nearly every industry imaginable. In 2019, demand for data scientists came from:
- Tech companies like Google, Amazon, and Facebook using data to optimize user experiences.
- Healthcare organizations analyzing patient data for better diagnoses and drug discovery.
- Financial institutions improving fraud detection and risk management.
- Retailers and e-commerce giants using predictive analytics to personalize shopping.
- Government agencies applying data to improve public services and security.
This widespread demand made the career future-proof and universally valuable.
3. Competitive Salaries
In 2019, Glassdoor reported that the median base salary for a Data Scientist was around $108,000 per year — significantly above the U.S. national average income. Senior-level data scientists often commanded salaries well above $150,000.
The combination of strong pay, job growth, and satisfaction created an irresistible package for ambitious professionals.
4. Scarcity of Skilled Talent
While demand skyrocketed, the supply of qualified data scientists lagged behind. Mastery of advanced statistics, machine learning, and programming required years of education and training. Universities and bootcamps were only beginning to catch up, meaning that skilled data scientists in 2019 had the luxury of choosing among multiple high-paying job offers.
5. The “Sexiest Job of the 21st Century” Effect
Back in 2012, Harvard Business Review published a famous article declaring data science the “sexiest job of the 21st century.” By 2019, that prophecy seemed fulfilled. The media attention and prestige attached to the title created a powerful aspirational image, drawing even more students into the field.
Breaking Down the Data Scientist Role
To understand why Glassdoor ranked it #1, we need to look closely at what a data scientist actually does.
Core Responsibilities
- Collecting & Cleaning Data: Preparing raw data for analysis.
- Exploratory Data Analysis (EDA): Identifying trends and patterns.
- Building Predictive Models: Using machine learning algorithms to forecast outcomes.
- Communicating Insights: Creating visualizations and reports for non-technical stakeholders.
- Driving Business Decisions: Turning insights into strategies that improve efficiency, profitability, or customer satisfaction.
Key Skills Required
- Programming (Python, R, SQL)
- Statistics & probability
- Machine learning & AI methods
- Data visualization (Tableau, Power BI, matplotlib, etc.)
- Business acumen and communication
Tools of the Trade (2019 snapshot)
- Languages: Python, R, SQL
- Frameworks: TensorFlow, scikit-learn, PyTorch
- Platforms: AWS, Azure, Google Cloud
- Visualization: Tableau, Power BI, matplotlib
Salary Trends in 2019
One of the biggest reasons Data Scientist took the top spot was its impressive salary range.
- Entry-level (0–2 years): $85,000 – $110,000
- Mid-level (3–5 years): $120,000 – $150,000
- Senior-level (5+ years): $150,000 – $200,000+
In comparison, software engineers at the time earned a median base salary of around $95,000, while financial analysts averaged $65,000. The premium on data science roles made them highly attractive to skilled professionals.
Industry Applications of Data Science in 2019
One of the strongest reasons Data Scientist ranked so high in 2019 was its universal relevance across industries. Unlike careers that are tied to specific sectors, data science was — and still is — a field that could be applied almost anywhere.
Let’s explore the key industries that heavily relied on data science back in 2019.
1. Technology & Internet Services
- Tech giants such as Google, Amazon, Facebook, and Microsoft were among the biggest employers of data scientists.
- They used data for ad targeting, recommendation systems, voice recognition, and image processing.
- Example: Netflix’s recommendation algorithm, powered by data science, was estimated to save the company $1 billion annually by reducing churn.
2. Finance & Banking
- In 2019, banks and financial institutions were increasingly investing in fraud detection, algorithmic trading, and credit risk modeling.
- Data scientists worked on predictive models that could flag unusual transactions in real time.
- Hedge funds and investment firms also employed them to optimize trading strategies.
3. Healthcare & Pharmaceuticals
- Healthcare was undergoing a digital revolution, with data scientists helping to predict disease outbreaks, personalize treatment, and accelerate drug discovery.
- Pharmaceutical companies used data to cut costs and improve efficiency in clinical trials.
4. Retail & E-Commerce
- Companies like Amazon, Walmart, and Alibaba relied on data science to predict demand, optimize logistics, and personalize shopping recommendations.
- Customer sentiment analysis through reviews and social media also became an important tool.
5. Manufacturing & Industry 4.0
- Predictive maintenance became a buzzword in 2019. Data scientists analyzed sensor data from machinery to prevent breakdowns before they happened.
- Supply chain optimization also benefited from advanced analytics.
6. Government & Public Services
- Governments began to rely on data science for public health monitoring, crime prediction, traffic optimization, and economic forecasting.
- Data-driven policymaking was a rising trend.
By 2019, it was evident that no industry could ignore data science. This universality gave the role unmatched staying power.
Job Satisfaction: Why Data Scientists Loved Their Work
Another critical factor that elevated Data Scientist to #1 was job satisfaction. Glassdoor’s data showed that data scientists consistently rated their roles highly, often giving scores above 4.0 out of 5.
Factors Contributing to High Satisfaction
- Meaningful Work
- Data scientists often worked on impactful projects, from improving patient care to fighting fraud.
- Many professionals felt they were contributing to innovation.
- Intellectual Challenge
- The role involved problem-solving, experimentation, and creativity.
- For analytical minds, this constant intellectual stimulation was deeply rewarding.
- Autonomy and Influence
- In many organizations, data scientists were trusted advisors to leadership.
- They had influence over high-stakes business decisions.
- Collaborative Work
- Data scientists worked with cross-functional teams: engineers, marketers, and executives.
- This variety kept the job dynamic and engaging.
- Strong Career Growth
- Opportunities for advancement were plentiful, with pathways to senior data scientist, machine learning engineer, or even Chief Data Officer.
Comparing Data Scientist with Other Top Jobs of 2019
To truly understand why it took the #1 spot, let’s compare it against other roles that also ranked highly on Glassdoor’s 2019 list.
Software Engineer (#2 in 2019)
- Similarities: Both required coding and problem-solving.
- Differences: Software engineers typically built products, while data scientists extracted insights from data.
- Why Data Scientist ranked higher: Broader industry application and higher satisfaction scores.
Product Manager (#5 in 2019)
- Similarities: Both worked cross-functionally and influenced business decisions.
- Differences: Product managers focused on business strategy, while data scientists focused on technical insights.
- Why Data Scientist ranked higher: Stronger salary averages and job demand.
DevOps Engineer (#3 in 2019)
- Similarities: Both were essential in modern tech companies.
- Differences: DevOps engineers focused on infrastructure automation, while data scientists worked on analytics and predictions.
- Why Data Scientist ranked higher: Greater media attention and prestige.
Dentist (#4 in 2019)
- Similarities: Both had strong salary potential.
- Differences: Dentistry required years of expensive schooling and had limited flexibility, while data science was accessible through multiple pathways (degrees, bootcamps, online learning).
- Why Data Scientist ranked higher: Broader career paths and less restrictive entry.
This comparison showed that while other jobs were excellent in their own right, Data Scientist offered the best balance of salary, demand, and satisfaction.
Challenges of Being a Data Scientist
Of course, the job wasn’t perfect. Even in 2019, data scientists faced unique challenges that balanced out some of the perks.
1. Ambiguous Job Definitions
- “Data Scientist” was a catch-all title. Some were doing advanced AI research, while others were building dashboards.
- This ambiguity sometimes led to frustration when expectations didn’t align with reality.
2. Data Quality Issues
- A common saying was: “80% of a data scientist’s job is cleaning data.”
- Messy, incomplete, or biased datasets often slowed progress.
3. High Expectations from Employers
- Many companies hired data scientists hoping for “magical insights,” without having the proper infrastructure.
- This mismatch caused stress and burnout.
4. Constant Need for Upskilling
- Technology evolved rapidly, requiring professionals to continuously learn new tools and algorithms.
- For some, this was exciting; for others, exhausting.
5. The Reality vs. Hype Gap
- By 2019, media hype had created unrealistic expectations about what data scientists could achieve.
- Some professionals felt burdened by the gap between perception and day-to-day work.
