Introduction — the moment data went mainstream
In 2019, Glassdoor named Data Scientist the top job in the United States. That ranking captured more than a momentary hiring trend — it reflected a larger structural shift: organizations across sectors moved from curiosity about data to operational dependence on it. High salaries, plentiful openings, strong employee satisfaction, and a persistent talent shortage combined to make data science both lucrative and influential.
This article dives deep into why Data Scientist reached the top of the list, what structural forces sustained its rise, the problems and growing pains the field faced, and what the future likely holds for practitioners and organizations. If you’re an aspiring data professional, a hiring manager, or simply curious about how careers evolve in the age of data, this article is for you.
How Glassdoor Ranks “Top Jobs” — the evaluation lens
Understanding Glassdoor’s criteria helps explain why certain roles rise to the top:
- Compensation: median base salary is a principal factor.
- Job openings: demand shown by the number of active listings.
- Job satisfaction: ratings and reviews by current and former employees.
- Opportunity: growth potential and career mobility.
A role that scores well across these measures — high pay, many listings, and satisfied employees — naturally ranks highly. In 2019, the Data Scientist role performed strongly in each category. But behind the numbers were several interlocking causes that made the role uniquely valuable.
1) Demand: Why organizations needed data scientists
Data as a strategic asset
By 2019, data was no longer a novelty; it was a competitive lever. Organizations sought to extract value from the massive, growing volumes of structured and unstructured information they accumulated. That included:
- Customer behavior and sales data for personalization and pricing.
- Operational telemetry for predictive maintenance.
- Clinical and genomic data for precision medicine.
- Financial transaction data for fraud detection and risk modeling.
The demand was cross-industry — tech, finance, healthcare, retail, manufacturing, and even government — which widened the job market substantially.
New product and revenue opportunities
Startups and incumbents alike realized that machine learning models could create new products (recommendation engines, personalization layers) and open revenue streams (targeted marketing, dynamic pricing). Companies hired data scientists to prototype, validate, and scale these features.
Decision-making shifted to metrics
Organizations moved toward OKR- and KPI-driven cultures. Data scientists became essential to define metrics, build dashboards, and run experiments that informed product and strategic decisions.
2) Compensation: why pay was strong
High demand + limited supply = wage premium. Data scientists often commanded generous base salaries and total compensation that included bonuses and equity, especially in tech hubs. Employers knew that an effective model could generate direct ROI — improved retention, increased conversions, cost savings — so they were willing to invest.
Compensation also varied by specialty and location:
- ML/Deep Learning roles in major tech centers often paid the most.
- Finance and quant roles frequently matched or topped tech offers.
- Remote and non-tech roles provided solid pay but sometimes with narrower upside.
For candidates, the salary premium reflected not only technical ability but also the capacity to deliver business impact.
3) Job satisfaction: why practitioners liked the work
Glassdoor’s rankings factor in employee sentiment. Data scientists often reported high job satisfaction because:
- The work was intellectually challenging and varied.
- Projects often had measurable impact.
- There was a strong sense of autonomy and creativity.
- Career paths could lead to senior analytics, data engineering, ML engineering, or product leadership roles.
Meaningful work plus visible impact is a rare career sweet spot — it explains why many data scientists rated their jobs highly.
4) Talent shortage: why supply lagged demand
Despite growing graduate programs and bootcamps, supply could not keep pace with corporate needs. Several factors caused this gap:
- High skill heterogeneity: employers wanted statistics, software engineering, domain knowledge, and product sense in a single hire.
- Experience premium: companies often preferred candidates with proven end-to-end project delivery, not just theoretical coursework.
- Rapidly evolving toolset: the tech stack (cloud platforms, MLOps, deep learning frameworks) was changing fast, creating a moving target for hiring managers.
This shortage amplified competition, driving further investment in hiring, training, and retention.
5) Role breadth: the versatility of “Data Scientist”
In 2019 the label “Data Scientist” functioned as a broad umbrella. It covered a spectrum of responsibilities:
- Exploratory data analysis and reporting.
- Statistical modeling and hypothesis testing.
- Building predictive models and production-ready machine learning systems.
- Data visualization and storytelling.
- Basic data pipeline work (ETL/SQL).
This breadth made the role useful across organizations — a generalist who could prototype analytics and then help operationalize it. Over time this breadth split into more specialized titles (data engineer, ML engineer, research scientist), but the early-phase generalist was critical to kickstart data initiatives at many firms.
6) Education and reskilling pathways: more routes into the field
A growth in educational offerings increased the labor pool:
- University master’s and specialized data-science programs.
- Online courses and microcredentials.
- Intensive bootcamps.
However, credentials alone were rarely sufficient. Employers prized portfolios and demonstrable end-to-end project experience. Candidates who paired formal education with real projects — open-source contributions, Kaggle competitions, or production prototypes — stood out.
7) Tooling and infrastructure: enabling scale
The maturation of cloud computing and analytic tooling made it feasible for companies to scale data projects:
- Cloud ML services and GPUs made model training more accessible.
- Data warehouse and lake solutions simplified data access.
- Version control, containerization, and CI/CD practices began to appear in analytics workflows.
Better tooling reduced friction — but it also raised expectations for what a data team could deliver. As infrastructure matured, organizations expected data scientists not only to prototype models but to ship them.
8) Organizational adoption: embedding analytics into business
A critical shift was that analytics moved from isolated proofs-of-concept to integrated product capability:
- Analytics teams were embedded in product units.
- Data-driven insights influenced marketing, operations, and strategy.
- Companies invested in data platforms and engineering to sustain models.
This shift created repeatable demand. Once a team proved a model’s ROI, the organization often scaled the capability — hiring more scientists and engineers.
9) Hype and signaling effects
The AI/ML hype cycle didn’t just attract talent — it attracted investment and strategic attention. Hiring data scientists became a signal to investors and markets that an organization was forward-looking. While hype sometimes created inflated expectations, it also accelerated real investment in analytics teams and platforms.
10) Industry deep-dives — how sectors used data scientists
Tech
Tech companies leveraged data scientists to optimize products: recommendations, ranking algorithms, ad auctions, user retention modeling. High scale and access to user behavior data made these roles central.
Finance
In finance, data scientists modeled risk, priced assets, and detected fraud. Quantitative skills—time series, stochastic processes—aligned well with financial needs, yielding high compensation.
Healthcare
Healthcare used data science for diagnostics, patient stratification, and operational efficiency. The field demanded domain expertise plus regulatory and ethical awareness.
Retail & E-commerce
Retail companies used data science for demand forecasting, inventory optimization, and personalization — areas with direct revenue impact.
Manufacturing & Energy
Predictive maintenance and process optimization reduced downtime and saved significant capex, turning data science into a cost-saver rather than a revenue-only function.
Across sectors, the common thread was measurable impact: data science projects that moved KPIs were prioritized and well compensated.
11) Growing pains: structural issues the field faced
The meteoric rise came with several friction points:
Role ambiguity
Many organizations had mismatched expectations: leadership wanted deployed models but hired scientists skilled in prototyping and research. The gap produced frustration and churn.
Siloed teams
Analytics sometimes lived in silos, separated from engineering and product teams. That made productionization slow and brittle.
Burnout and unrealistic timelines
Pressure to generate quick wins led to overwork, especially when models had to transition from lab to production under tight deadlines.
Ethical and regulatory challenges
As models touched sensitive domains (hiring, lending, healthcare), concerns about bias, transparency, and fairness became urgent. Data scientists increasingly needed to engage with ethics and compliance.
Talent homogeneousness
An over-reliance on specific pipelines (PhDs, bootcamps) risked a lack of diversity in thinking and background, limiting creativity and leading to groupthink in model design.
12) The evolution of job roles: specialization and new titles
By the end of the decade the “data scientist” label began to fragment into clearer roles:
- Data Engineer: responsible for pipelines, data quality, and platforms.
- Machine Learning Engineer: focuses on productionizing models, MLOps, and scalability.
- Research Scientist: focuses on bleeding-edge model development and R&D.
- Data Analyst / BI Analyst: focuses on reporting and dashboarding.
- Applied Data Scientist: combines modeling with applied domain problems.
This specialization improved productivity by aligning skills with tasks, but it also required clearer hiring strategies.
13) How to hire a data scientist (for employers)
To hire effectively, organizations needed to rethink job descriptions and interview processes:
- Define the problem, not the title. Start with the business problem you want solved and map the required skills — modeling? productionization? domain expertise? — then craft a role accordingly.
- Test for impact. Use take-home projects or case studies that simulate real problems. Focus on end-to-end thinking: data sourcing -> modeling -> deployment -> metrics.
- Assess communication. Data scientists must explain technical work to stakeholders; include behavioral interviews focused on storytelling with data.
- Invest in infrastructure. Clear paths for models to succeed (data, compute, engineering support) are essential to retain talent.
- Offer growth and mentorship. Career ladders and learning budgets reduce churn.
14) How to become a data scientist (for job seekers)
If you’re aiming for a data science career, adopt a practical, project-first approach:
- Master the fundamentals: statistics, probability, linear algebra, and a programming language (Python or R).
- Build a portfolio: end-to-end projects that show problem framing, data cleaning, modeling choices, validation, and interpretation.
- Learn engineering basics: SQL, version control (git), and familiarity with cloud platforms.
- Get domain knowledge: understanding the business context separates good models from irrelevant ones.
- Practice communication: write clear explanations, create dashboards, and present results.
- Contribute to open source or competitions: Kaggle, GitHub projects, and community contributions show initiative.
- Network and mentorship: talk to practitioners, attend meetups, and seek mentors who can advise and open doors.
15) Portfolio playbook: what hiring managers want to see
Create a small set of polished projects:
- One project that demonstrates solid statistical thinking (A/B testing, causal inference).
- One project that shows machine learning modeling and evaluation (classification/regression with careful validation).
- One project that demonstrates engineering and reproducibility (containerized app, notebook -> script -> API).
- Documentation: a clear README, results dashboard, and an explanation of business impact.
Quality over quantity — a few polished projects beat many half-finished ones.
16) Compensation strategy: negotiating like a pro
When negotiating:
- Know the market range for your domain and location.
- Emphasize impact: quantify how your work improved KPIs or saved costs.
- Ask about total compensation (salary + bonus + equity + benefits).
- Consider non-monetary perks that matter: learning budgets, remote flexibility, and project autonomy.
For employers, offering career development and interesting problems often matters as much as headline pay.
17) The ethics and governance imperative
As models affected lives, organizations had to develop governance:
- Model auditing for bias and fairness.
- Explainability for regulated domains.
- Data privacy alignment with laws like GDPR and sector-specific rules.
- Human-in-the-loop safeguards where errors could be harmful.
Ethical maturity became a competitive differentiator: organizations that did it well avoided costly backlash and built trust.
18) Remote work, freelancing, and the gig economy
2019’s momentum preceded the broad shift to remote work accelerated in 2020. That created new labor market dynamics:
- Remote roles opened opportunities beyond tech hubs.
- Freelance data scientists and consultants grew, serving companies that needed short-term expertise.
- Contract-to-hire models became common for specialized projects.
Remote work broadened the candidate pool but also increased competition.
19) Case studies (conceptual examples, not specific company endorsements)
Case — E-commerce personalization
A mid-sized e-commerce company hired a small data science team to build a recommendation system. Within six months, conversion rates on personalized pages rose measurably. The ROI justified expanding the team and investing in real-time feature infrastructure.
Case — Manufacturing predictive maintenance
A manufacturing firm used sensor data and time-series models to predict equipment failure. Reduced downtime translated directly into increased production uptime and lower maintenance costs, justifying further analytics investments.
Case — Healthcare triage model
A hospital piloted a model to prioritize patients for follow-up care. The project required clinical collaboration and careful bias testing. Though deployment was phased, the model improved resource allocation and patient outcomes.
These examples show the pattern: identify a measurable business pain, develop a model that addresses it, and measure impact thoroughly.
20) The future: where the field is heading
Several trends shaped the medium-term outlook:
Specialization continues
Expect clearer role definitions: MLOps engineers, causal inference specialists, and domain-specific data scientists (healthcare, finance).
Automation and tooling improve productivity
AutoML, better MLOps platforms, and low-code ML tools will make prototyping faster, but they won’t replace the need for creativity, problem framing, and domain understanding.
Data governance matures
Organizations will formalize model monitoring, retraining policies, and ethical review boards.
Interdisciplinarity grows
Data science will increasingly intersect with design, product management, and regulation. Soft skills (communication, leadership) will matter as much as technical prowess.
Lifelong learning as the norm
Given the pace of change, practitioners must commit to continuous upskilling.
21) Challenges to watch
- Commoditization risk: as tools make models easier to build, some parts of the work may become commoditized, pushing talent toward higher-value tasks.
- Talent distribution: companies that invest in talent pipelines (internships, training) will maintain an edge.
- Regulatory uncertainty: evolving laws may change what models can be used in certain sectors.
- Public trust: misuse of models can lead to reputational damage and reduced adoption.
22) Practical roadmap for organizations building data capability
- Start with problems, not tech. Identify 2–3 high-impact use cases.
- Invest in data platform basics. Clean, reliable data is the foundation.
- Hire for complementary skills. Mix modelers with engineers and product people.
- Build a realistic production path. Plan for deployment, monitoring, and retraining.
- Create ethical guardrails. Implement audits and human review where necessary.
- Measure impact. Use experiments and dashboards to prove value.
Companies that follow this roadmap avoid many common pitfalls and scale analytics more effectively.
23) FAQs (search-optimized)
Q: Was Glassdoor’s #1 ranking in 2019 simply hype?
A: No. While AI/ML hype helped visibility, the ranking reflected measurable demand, salary premiums, and user-reported job satisfaction — structural elements that made the role genuinely attractive.
Q: Do I need a PhD to become a data scientist?
A: Not necessarily. Many roles require strong math and programming skills. Master’s programs, bootcamps with solid project portfolios, and domain experience can also lead to data science careers.
Q: What’s the difference between a data scientist and a data engineer?
A: Data scientists focus on analysis and modeling; data engineers build and maintain the data pipelines and infrastructure that enable scalable analytics. Both roles are complementary.
Q: Is data science a bubble?
A: Parts of the market may cool or specialize, but the underlying need for data-driven decision-making is durable. The field is likely to mature rather than vanish.
Q: Which industries pay the most for data scientists?
A: Historically, tech, finance/quant roles, and specialized healthcare or biotech positions offered top pay. Compensation varies by location, experience, and the specific skills needed.
Q: How important is domain knowledge?
A: Very. Domain expertise accelerates the pathway from a model to real business value. Combining technical skill with industry knowledge makes candidates stand out.
24) SEO & WordPress publishing tips
- Title tag: Keep under 60 characters while including the keyword: Glassdoor 2019 Top Job — Why Data Scientist Was #1.
- Meta description: 150–160 characters summarizing the angle and including “Data Scientist” and “Glassdoor 2019”.
- H1: Use the main headline (already set).
- Subheadings: Use descriptive H2/H3s to improve scannability.
- Internal links: Link to related posts (e.g., “How to Build a Data Science Portfolio”, “MLOps 101”).
- External links: Link to authoritative sources for Glassdoor reports, professional organizations, and major academic programs if you reference facts or data externally.
- Featured image: Use an evocative but non-cliché image — a modern office, a person studying data visualizations, or an abstract visualization of networks.
- Schema: Add Article schema with author, publish date, and meta tags for better SEO.
25) Suggested related posts to link internally
- How to Build a Data Science Portfolio (step-by-step)
- Data Scientist vs. ML Engineer: Which Path Should You Choose?
- Top Tools for Data Engineering in 2024
- Ethics in AI: A Practical Guide for Teams
- Negotiating Tech Salaries: A Playbook
26) Conclusion — a structural shift, not a fad
Glassdoor’s 2019 ranking of Data Scientist as the top U.S. job captured a structural career inflection: businesses were committing to data-driven ways of working, and they needed talent to deliver on that pledge. High pay, strong demand, job satisfaction, and a talent gap created the perfect storm that put data science atop career lists.
Since then the field has matured — roles specialized, tooling improved, and governance became more important. For job seekers, opportunities remain rich but require adaptability and demonstrable impact. For organizations, success depends on hiring the right mix of skills and building platforms and processes that let models deliver measurable outcomes.
Data science wasn’t a passing fad in 2019 — it was the start of a long-term transformation in how we use information to make decisions. The lessons from that era remain relevant: solve real problems, measure impact, and build teams that balance deep technical skill with product and domain sense.
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