Computing & Data
How to become a Data Scientist
Also known as Machine Learning Engineer, Applied Scientist. Everything below comes from the same database our assessment scores against.
The route in
1. Undergraduate
3 years- Qualifications:
- B.Tech Computer Science, B.Sc. Statistics, B.Sc. Mathematics, B.Stat
- Entrance exams:
- JEE Main, ISI Admission Test, CUET, State CET
- Typical cost:
- ₹1.5 lakh to ₹25 lakh
2. Postgraduate
2 years- Qualifications:
- M.Sc. Data Science, M.Tech Computer Science, M.Stat
- Entrance exams:
- GATE, CUET-PG, University entrance
- Typical cost:
- ₹1.5 lakh to ₹15 lakh
Subjects you must have taken
Mathematics. Without these the direct route is closed, though a bridging or open-school route often reopens it.
What does a Data Scientist earn in India?
Entry level
₹6 lakh – ₹18 lakh
per year
Mid career
₹18 lakh – ₹60 lakh
per year
Indicative. These bands are an editorial estimate assembled from public reporting, and have not yet been reconciled against a named dataset. Treat them as a shape, not a promise — the same title pays very differently across a service company, a product company and a start-up.
Outlook
Hiring demand
High growth — one of the faster-expanding areas in the country.
Exposure to automation
Medium. Parts of this role are already being automated, and the work is shifting toward the pieces that are not. Expect the job to change rather than disappear.
What does a Data Scientist do?
What this actually looks like
A day as a Data Scientist
What you would actually be doing
- Cleaning and joining data, which is most of the job and nobody mentions it
- Working out whether the question you were asked is the question worth answering
- Building a model, then finding out the simple approach was almost as good
- Explaining uncertainty to somebody who wants a single number
- Putting a model into production and watching it behave differently there
Who you work with
- Engineers who own the data pipelines
- Business stakeholders who own the decision
- Other analysts and scientists, in review
What you work with
- Python or R, and SQL constantly
- Notebooks for exploration, proper code for anything that ships
- Cloud data platforms and machine-learning tooling
Where and when: Desk work, often hybrid. Project-shaped rather than continuous, and a large share of the week goes on data preparation rather than on modelling.
Working conditions
- Typical hours
- 9 hours
- Remote possible
- Yes, at least partly
- Travel
- low travel
- Physical demand
- low physical demand
Reality check
Before you choose data scientist
A report that only lists what is appealing about a career is an advertisement. This is the other half.
What people like about it
- You find things out that change what an organisation does
- The mathematics is genuinely interesting and keeps developing
- Demand is strong across almost every sector
- The skills transfer between industries with little friction
What is genuinely hard
- Most of the work is data cleaning, not modelling, and that surprises people
- Many projects never reach production, for reasons outside your control
- The title covers wildly different jobs, so read the description carefully
- Keeping current with the field is continuous and largely unpaid
- Who tends to enjoy this work
- Are genuinely curious about why a number moved, comfortable saying an analysis was inconclusive, and patient enough to spend three days preparing data for one day of analysis.
- What the education journey is really like
- Usually a quantitative degree — statistics, mathematics, computer science, economics or engineering — and often a master's. Portfolio projects on real, messy public datasets count for a great deal at the hiring stage, more than certificates do.
Other legitimate ways in
The first route does not always work, and it is not always the best one. These are real alternatives, not consolation prizes.
Quantitative degree into data science
- Degree in statistics, mathematics, economics, engineering or computer science
- Data analyst or junior data scientist role, with SQL and Python
- Data scientist, then senior or specialised machine learning roles
The analyst step is worth taking. Business context is what makes the modelling useful.
Domain expert into data science
- Deep knowledge of a domain — finance, health, supply chain, marketing
- Statistics, Python and machine learning skills added deliberately
- Data science role in that domain, where the context is the advantage
Often stronger than a pure technical route, because framing the question is the hard part.
Who may not enjoy being a Data Scientist?
Nothing here is a judgement about you — it is what the job is like, listed so you can decide whether it is what you want.
- The hours look like this: 9 hours.
- The route is long: about 5 years of study before you are earning from it.
The four hardest things about the work itself are listed above, under what is genuinely hard. If those and the points here describe things you would rather not sign up for, that is a real answer and worth having before a stream or a degree is chosen around it.
What skills does a Data Scientist need?
What the job actually asks for, split four ways. The assessment tells you which of these your own scores already speak to.
Technical skills
- Statistics and experimental design
- Feature engineering and model selection
- Validating a model honestly, including on data it has not seen
- Framing a business question as a measurable one
People skills
- Telling a stakeholder the data does not support their plan
- Explaining a model's limits without hiding behind the method
- Resisting a result that is too good to be true
Digital and AI skills
- Python or R, with SQL as a given
- Machine learning frameworks and experiment tracking
- Data pipelines, warehouses and deployment
Industry knowledge
- Where data scientist, data analyst and ML engineer roles actually differ
- Which industries have mature data functions and which are still reporting
- What gets a model into production, and why most never get there
If Data Scientist appeals, also look at
Chosen by how far the interest pattern and the weighted activities of each one overlap with this. The reason is printed so you can disagree with it.
Computing & Data
People move between these two often. Experience in one counts toward the other. Both use quantitative.
Finance & Commerce
People move between these two often. Experience in one counts toward the other. Both use quantitative.
AI / Machine Learning Researcher
Computing & Data
People move between these two often. Experience in one counts toward the other. Both use quantitative.
Computing & Data
Uses the same computing & systems and inquiry & research skills. You get in a different way.
Computing & Data
People move between these two often. Experience in one counts toward the other. Both use quantitative.
Streams this is reachable from
Exams worth knowing about
Where an exam has its own page, it lists every other career it opens — useful if this one turns out not to fit.
Where this qualification also leads
The degrees on this route open other doors too. Worth knowing before you commit to one destination.
Who tends to be happy here
People who rate these highly tend to stay in this work. If none of them sound like you, that is worth taking seriously.
- Achievement
- Independence
- Recognition
Are you sure Data Scientist is the career you want?
Everything above describes the job. None of it tells you whether it fits you. So it is worth asking plainly, before a stream or a degree gets chosen around it.
- Not sure? Then this is exactly the question the assessment exists to answer. It compares you against Data Scientist and 196 other careers, and shows the arithmetic behind every score, so you can see why rather than being handed a verdict.
- Quite sure? Then double-check anyway. Being certain is not the same as having checked, and the cost of finding out late is counted in years rather than in the 45 minutes this takes. If it confirms what you already think, you carry on with a reason instead of a hope — plus the education route, the entrance exams and a ninety-day plan.
And if it does not line up, nothing closes. Data Scientist stays on your list with the gap named and a route attached, next to the careers your profile already reaches — so the answer is something to act on either way.
Other Computing & Data careers
Last verified 2026-08-01. This is a career-guidance instrument. It is not a clinical or diagnostic tool. It does not measure intelligence, IQ, or a person’s worth. Its results are one input among several, and are intended to be interpreted with a trained counsellor.