The Data Science Bubble: Why 80% of Data Scientists Do Excel Work
Key Takeaways
- This piece focuses on data science realities in India, not outlier narratives.
- Compensation numbers should be interpreted with role scope, market cycle, and switching friction.
- Use decision frameworks and evidence checks before acting on title or salary headlines.
On This Page
The Expectation
The Data Science Dream:- Build AI/ML models that change the world
- Work on cutting-edge algorithms
- Six-figure salary for writing Python
- Be the smartest person in the room
What Bootcamps Sell: Learn TensorFlow, build neural networks, become a data scientist in 6 months. Jobs everywhere. Companies desperate for talent.
The Reality
Reality Check - What Data Scientists Actually Do:📊 Data Scientist Time Allocation (Industry Survey 2024)
| Activity | Bootcamp Promise | Actual Reality | At FAANG |
|---|---|---|---|
| Building ML Models | 60% | 10% | 25% |
| Data Cleaning/Wrangling | 10% | 45% | 30% |
| SQL Queries | 5% | 20% | 15% |
| Dashboards/Reporting | 5% | 15% | 10% |
| Meetings/Communication | 10% | 10% | 15% |
| Model Deployment/MLOps | 10% | 0% | 5% |
The Title Inflation Epidemic:
In 2015, there were about 10,000 "Data Scientists" in India. By 2024, there are 200,000+. Did data science work increase 20x? No. Companies renamed existing roles for:
- Better hiring (everyone wants to be a data scientist)
- Higher salaries for the same work
- Marketing to clients ("We use data science!")
Reality: 70% of "Data Scientists" are glorified Business Analysts with Python.
📈 What Companies Call "Data Science"
| What You Think | What It Actually Is | % of Roles |
|---|---|---|
| Building Neural Networks | Writing SQL queries | 40% |
| Training LLMs | Making PowerPoint charts | 25% |
| Deep Learning Research | Cleaning Excel files | 20% |
| Actual ML Development | Actual ML Development | 15% |
The Education-Industry Gap:
What bootcamps teach: TensorFlow, PyTorch, Deep Learning, Neural Networks, Computer Vision What jobs actually need: SQL, Pandas, Excel, Tableau, Basic Statistics, Communication
Case Study - The ML PhD Who Quit:
Amit, PhD in Machine Learning from IIT, joined a "Data Science" role at a unicorn startup. Reality:
- Week 1: Setting up Tableau dashboards
- Month 1: Writing SQL queries for marketing reports
- Month 3: Realized 90% of work is ad-hoc business queries
- Month 6: Quit for an actual ML Engineer role at 30% lower salary
The title said "Data Scientist." The job was "Business Intelligence Analyst."
The Skills That Actually Get Used:
📊 Skills Usage in "Data Science" Roles
| Skill | How Often Used | How Much Studied |
|---|---|---|
| SQL | Daily | 1 week in bootcamp |
| Excel/Sheets | Daily | Often skipped |
| Python basics | Weekly | Moderate |
| Communication | Daily | Never taught |
| Deep Learning | Rarely | 50% of bootcamp |
| NLP/LLMs | Almost Never | 20% of bootcamp |
Related context: Salary Reality Check, CTC Decoder, more in Data Science.
Salary and Growth Reality
💰 Salary by Actual Work Done (Not Title)
| Actual Role | 0-2 Years | 3-5 Years | 6+ Years |
|---|---|---|---|
| ML Engineer (Real) | Rs 15-25 LPA | Rs 30-50 LPA | Rs 50-80 LPA |
| Data Scientist (Research) | Rs 12-20 LPA | Rs 25-40 LPA | Rs 45-70 LPA |
| DS (Analysis Focus) | Rs 8-15 LPA | Rs 15-25 LPA | Rs 25-40 LPA |
| Analyst w/ DS Title | Rs 6-10 LPA | Rs 12-18 LPA | Rs 18-28 LPA |
The Top 5% vs Bottom 95%:
The salaries you see on LinkedIn and in news articles? Those are for the TOP 5% - people at FAANG, doing actual ML at scale.
For most people with "Data Scientist" in their title:
- Starting: Rs 6-10 LPA
- 3 years: Rs 12-18 LPA
- 5 years: Rs 18-25 LPA
This is analyst money with fancier title.
Cross-check your take-home with the CTC Decoder and compare ranges in Salary Reality.
Where Most People Get Stuck
Where DS Aspirants Get Permanently Trapped:Trap 1: The Kaggle Paradox You can win Kaggle competitions but cannot write production code. Companies need code that runs in prod, not notebooks that win medals. Kaggle is training you for a job that barely exists.
Trap 2: The Framework Obsession You know PyTorch AND TensorFlow AND JAX. You cannot write a SQL join. 95% of DS interviews have SQL rounds. You fail them.
Trap 3: The Research Fantasy You want to do "research" like you read about. Real DS research roles: maybe 500 positions in all of India. You are competing with PhDs from top global universities.
Trap 4: The Tool Collector Your resume lists: Python, R, SQL, Tableau, Power BI, Spark, Hadoop, TensorFlow, PyTorch, Keras, Scikit-learn... You are master of none. Depth beats breadth.
Breaking Free:
For actual ML work:
- Target ML Engineer titles specifically
- Focus on deployment (Docker, Kubernetes, MLflow)
- Join companies with real ML in production (not "AI-powered" marketing)
For well-paying analyst work:
- Accept the reality, optimize for it
- Master SQL, communication, business metrics
- Move toward Analytics Manager track
The middle path (vague "Data Scientist") leaves you in no-man's-land.
If this matches your current situation, run the Resignation Risk Analyzer before making your next move.
Who Should Avoid This Path
If you work at a FAANG company building actual ML systems, this does not apply to you. You are in the 5% doing real data science.Decision Framework
Use this quick framework before changing role, company, or specialization.
- If your take-home is not compounding with experience, benchmark externally before accepting internal narratives.
- If role expectations keep rising without title/pay movement, escalate with documented outcomes.
- If growth path is unclear beyond 6-9 months, run a switch-or-specialize decision cycle.
Common Mistakes Checklist
- Treating outlier salaries as planning baselines.
- Using title changes as a substitute for capability changes.
- Delaying market benchmarking until after compensation stagnates.
- Over-indexing on model demos without production deployment depth.
Real Scenario Snapshot
A professional stays in-role despite rising responsibility and flat pay. Growth recovers only after external benchmarking and a deliberate switch-or-specialize decision.
Originality Lens
Contrarian thesis: Career outcomes usually degrade from quiet trade-offs, not sudden failures.
Non-obvious signal: When responsibility rises but decision rights stay flat, stagnation risk rises even before pay slows.
Evidence By Section
Claim: Popular career narratives overweight edge cases and underweight base-rate outcomes.
Evidence: AmbitionBox Salary Insights, Glassdoor India Salaries
Claim: Observed market behavior diverges from social-media compensation storytelling.
Evidence: Glassdoor India Salaries, LinkedIn Jobs (India)
Claim: Salary and growth ranges vary by company type, leverage, and cycle timing.
Evidence: AmbitionBox Salary Insights, Glassdoor India Salaries, LinkedIn Jobs (India), Naukri Jobs (India)
Claim: Career plateaus are often linked to stale scope, weak mobility planning, and evidence gaps.
Evidence: LinkedIn Jobs (India), Naukri Jobs (India), Kaggle State of Data/AI
Final Verdict
Data Science is real. But "Data Science jobs" are mostly not Data Science.
If you want actual ML work:
- Target FAANG, well-funded AI startups, research labs
- Accept Rs 0 during PhD or research fellowship
- Build deployed projects, not notebooks
- Apply for "ML Engineer" not "Data Scientist"
If you want good money and work-life balance:
- Accept the analyst reality
- Optimize SQL, BI tools, communication
- Move toward Analytics Lead/Manager
- Stop chasing the ML dream that company cannot use
If you are already in a "DS" role doing analyst work: You have two choices:
- Accept it, grow within it, become Analytics Manager
- Skill up for real ML roles, be prepared to job hunt for 6+ months
What does NOT work: Staying frustrated in a mismatch, collecting more certificates, hoping it changes.
The Test: On your current project at work, do you use:
- Neural Networks? → You are doing actual DS
- Gradient Boosting at scale? → Maybe actual DS
- SQL and Tableau? → You are an analyst (that is okay!)
Stop letting job titles lie to you.
What Changed
- January 12, 2026: Reviewed salary ranges, corrected stale assumptions, and tightened internal links for related reads.
- January 12, 2026: Initial publication with baseline market framing and trade-off analysis.
Sources
- AmbitionBox Salary Insights (checked February 22, 2026)
- Glassdoor India Salaries (checked February 22, 2026)
- LinkedIn Jobs (India) (checked February 22, 2026)
- Naukri Jobs (India) (checked February 22, 2026)
- Kaggle State of Data/AI (checked February 22, 2026)