The Junior Data Science Reality: You Are a SQL Janitor

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 Reality
• Salary and Growth Reality
• Where Most People Get Stuck
• Who Should Avoid This Path
• Decision Framework
• Originality Lens
• Final Verdict

The Expectation

The expectation is sold to you by EdTech influencers and Coursera certificates.

"Data is the new Oil."

You expect to walk into a company and be handed a perfectly clean, labeled dataset. You expect the Business Stakeholders to ask you for "Predictions" and "Insights".

You imagine your workflow like this:

• Import Data
• Train Model
• Optimize Hyperparameters
• Present cool 3D graphs to the CEO
• Get promoted for increasing revenue by 20%

You think 80% of your time will be spent on Modelling and 20% on deployment.

You think SQL is "legacy tech" for backend engineers, and Excel is for finance guys.

The Reality

What Junior Data Scientists Actually Do:

The SQL Janitor Reality:

70-80% of junior data science work is:

• Writing SQL queries to pull data
• Cleaning data that's never clean
• Building reports and dashboards
• Answering "can you pull this data?" requests
• Diagnosing why numbers don't match

The machine learning you studied? You'll use it on 5-10% of your tasks. And that's if you're lucky enough to have problems that need ML rather than simple analytics.

Case Study - The ML Dreamer:

Priya, 25, Junior Data Scientist at E-commerce Startup:

• Masters in ML from good institute
• Expectation: Building recommendation systems
• Reality: "Can you pull last month's sales by category?"
• ML projects worked on in 18 months: 1
• SQL queries written: Hundreds
• Dashboards built: 15+
• Current feeling: "I'm a well-paid data analyst, not a data scientist"

Related context: Salary Reality Check, CTC Decoder, more in Data Science.

Salary and Growth Reality

Cross-check your take-home with the CTC Decoder and compare ranges in Salary Reality.

Where Most People Get Stuck

Where Junior Data Scientists Get Stuck:

The Analytics Trap:

You're good at SQL and dashboards now. You're valuable for that work. Company doesn't want to train you on ML—they need the reports done. You become a specialist in precisely what you didn't want to do.

The Portfolio Gap:

Your Kaggle projects are from bootcamp. Your work projects are all internal dashboards. When you interview for "real" DS roles, you can't show ML production experience.

Escape Routes:

1. Target ML Engineering: More engineering, less ambiguity. The work is what it claims to be.
2. Join AI-First Companies: Startups where ML is the product, not a nice-to-have.
3. Build Open Source/Side Projects: Create ML portfolio outside of work. Prove you can do the interesting stuff.
4. Research Roles: Academic or industry research labs. Lower pay, real ML work.
5. Specialize in DS Infrastructure: MLOps, feature stores, model serving. Less glamorous, more real demand.

If this matches your current situation, run the Resignation Risk Analyzer before making your next move.

Who Should Avoid This Path

Data Science Is Wrong For You If:

• You only want to build ML models: 70% of the job isn't that
• You hate SQL and data cleaning: That's most of the work
• You expect research-style work: Production constraints rule
• You joined because of course hype: Reality doesn't match marketing
• You want clear deliverables: DS projects are often ambiguous and fail

The Data Role Clarification:

If you want ML, target ML Engineering. If you're okay with analytics + occasional ML, Data Scientist works. If you want pure analytics, save yourself the ML courses and own the Data Analyst identity.

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