AI writes the code now, fresher hiring is flat and the pay numbers online disagree by a factor of two. Here is what the data says this month, and the order I would learn things in if I were starting today.
I first published this on Medium. Cross-posting here because a lot of the people asking me whether data science is still worth it are on DEV.
By Pankit Kumar, Senior Data Scientist and Lead Instructor at NeuraPath Academy
Yes, data science is still worth it in India in 2026. But it is worth it for a narrower group of people than the advertisements suggest, and the entry door has moved.
Here is the short version. Demand for AI and machine learning work in India is growing faster than most categories on the job portals. Hiring of freshers in general is flat. Employers are paying for people who can take a model from a notebook to something a business uses, and they are paying much less for people who can only train one. Many freshers will not start with the title "Data Scientist". They start as analysts or as junior data scientists at five to seven lakh a year, and the steep part of the pay curve comes after two or three years of real work.
If that sounds acceptable to you, read on. I will show you the numbers I read this week, my view on AI and the job, and the order I would learn things in.
What does the hiring data say right now?
The most current Indian number I trust is Naukri's monthly JobSpeak index, because it counts actual postings on Naukri, one of India's largest job portals. The September 2026 edition, published on 6 October, reported that "AI/ML roles continued to record growth, rising 20% YOY in September" (Naukri JobSpeak). That is year on year, against September 2025, and it is a count of postings, not of people hired.
The same report has the number freshers should read twice. Fresher hiring across all sectors was "+1% YOY", while demand for people with 8 to 12 years of experience grew 5% and for 13 to 16 years grew 7%. Hitesh Oberoi, the CEO of Info Edge, summed it up: "September's numbers reflect a hiring market that remains steady, but more selective." He added that demand "continued to move towards specialised skills, particularly AI, and more experienced professionals."
Put those two facts side by side. AI work is growing. Entry-level hiring is not. The field is healthy, and the first rung of the ladder is crowded.
Two global sources point the same way, with caveats you should know. The World Economic Forum's Future of Jobs Report 2025 is built on a survey of employers, and they placed "Big Data Specialist" and "AI and Machine Learning Specialists" at the top of their fastest growing roles to 2030 (WEF, Future of Jobs 2025). That is what employers expect, not a count of jobs. The US Bureau of Labor Statistics projects that "Employment of data scientists is projected to grow 35 percent from 2025 to 2035" (BLS Occupational Outlook, page updated 27 August 2026). That is a US projection. I quote it because it is careful and official, not because it describes Indian hiring.
Will AI replace data scientists?
No. It will replace a large part of what junior data scientists used to be paid to do, and that difference matters for how you prepare.
Look at how programmers already work. In the 2025 Stack Overflow Developer Survey, "84% of respondents are using or planning to use AI tools in their development process." In the same survey, "More developers actively distrust the accuracy of AI tools (46%) than trust it (33%)," and the biggest single frustration, cited by 66%, was "AI solutions that are almost right, but not quite" (Stack Overflow 2025). That survey is of developers, not data scientists, but anyone who writes pandas code for a living will recognise the pattern.
"Almost right" is the whole story for our field. An assistant will write a cleaning script, a train-test split and a gradient boosting model in a minute. It will also leak tomorrow's data into today's features, pick accuracy as the metric on a dataset where 2% of rows are fraud, and present the result with total confidence. The code runs. The answer is wrong.
So the work that is shrinking is the typing. The work that is growing is the judgement: is this the right question, is this the right metric, did the data leak, would I sign my name under this number? I have spent ten years in data science, much of it on production systems in regulated pharma and clinical settings. In that world a model output nobody can explain does not ship, however good the accuracy looks. That habit used to be a specialist's habit. With AI writing the first draft of everything, it is now the core skill.
My position, then, is simple. If your plan was to become the person who writes routine models on request, AI has made that plan weaker. If your plan is to become the person who can tell whether a model is right and get it used, AI has made you more productive and more valuable.
What do freshers actually earn?
Ignore any single number you see in a headline. Pay for data roles in India varies hugely by title, experience, company type and city, and the figures online mix all of them together.
I read the public salary pages on AmbitionBox and PayScale on 17 October 2026 and put the experience-wise averages on one chart.
Reported annual pay by title and experience. Source: AmbitionBox experience-wise averages, updated 14 to 16 October 2026, and PayScale India, updated 9 July 2026, both read 17 October 2026.
A few things jump out.
The title changes everything. On AmbitionBox, a Junior Data Scientist with 0 to 1 year of experience reports ₹4.6 to 6.3 lakh a year. That band rests on only 52 salaries, so treat it as a rough signal. The 1 to 3 year band for the same junior title is ₹5.9 to 6.8 lakh. But someone holding the plain Data Scientist title with 1 to 3 years reports ₹11.3 to 12.5 lakh, from about 24,000 salaries. That gap is mostly about who gets the title, not about the same person being paid double.
PayScale lands close to the junior figures. Its India page says an entry-level data scientist with under a year of experience earns an average total compensation of ₹5,95,255, based on 307 salaries (PayScale India).
Data analysts start lower and rise more slowly. AmbitionBox shows ₹5.2 to 5.7 lakh at 1 to 3 years and ₹7.2 to 7.9 lakh at 3 to 6 years. Data scientists with 3 to 6 years report ₹15 to 16.6 lakh, and 6 to 9 years ₹20.5 to 22.6 lakh.
Now the part almost every fresher gets wrong: fixed pay versus CTC. In India, cost to company usually bundles your fixed salary with variable pay, the employer's provident fund share, gratuity and sometimes a one-time joining bonus. What reaches your bank account each month is noticeably less than CTC divided by twelve. PayScale separates base salary from total pay on its page. AmbitionBox does not label its averages as fixed or CTC, and shows monthly in-hand pay separately. So when two people compare "packages", ask which number they mean before you feel bad.
One more caution. These are self-reported averages from people who already hold the job. They tell you what the market pays, not what you will be offered.
Should you start with data science or data analytics?
For most freshers and career switchers, I would start with analytics. I don't say that because analytics is easier or a consolation prize. I say it because data science is built on top of it.
Look at what a data scientist does in a normal week. Pulling and joining data in SQL. Checking whether the numbers make sense. Explaining a result to someone who controls a budget. Building a model is often a smaller part of the week than people expect. An analyst role trains all of that, gets you paid while you learn it, and gives you real business data to practise on.
We built our own Data Science programme on this logic. The syllabus on our course page opens with a condensed Data Analytics Core of 40 live hours, covering Excel, SQL, Power BI, Python, EDA and statistics, before any machine learning. The page states the reason plainly: "Data science sits on top of the analyst stack; skipping the foundation is the single most common reason people stall." The same page tells anyone with no data experience at all that we would "honestly recommend beginning with analytics first."
If you already work with data, as an analyst, in finance, in operations, in a lab, then go straight at data science. You have the foundation and the domain, and the domain is worth more than most people realise.
What would I learn now, and in what order?
Here is the order I would follow if I were starting again today. It is my opinion, built from what I see employers ask for and from what I watch learners struggle with.
My recommended learning order, with what to postpone. An opinion, not survey data.
1. SQL, spreadsheets and one BI tool. Joins, group-bys and window functions, until you can answer a business question from raw tables without help. Then build one dashboard that a real person uses for a month. This is the skill you will use every single day.
2. Statistics and Python for analysis. Distributions, sampling, hypothesis tests, confidence intervals, and pandas until it is boring. Do honest exploratory analysis: what is missing, what is odd, what would change the answer. AI tools will write the pandas for you. They will not tell you that the "spike" in March is a duplicated file.
3. Classical machine learning, done properly. Linear and logistic regression, decision trees, random forests and gradient boosting. Spend more time on validation than on algorithms: cross-validation, leakage, class imbalance, choosing a metric the business cares about. Most real tabular problems in Indian companies are still solved here.
4. Ship one model. Wrap a model in a small API with FastAPI or a simple app with Streamlit, put it in Docker, and add basic logging so you can see when its inputs drift. Our own syllabus gives this its own module, Model Deployment, with Flask/FastAPI services, Streamlit apps, a Docker introduction and monitoring basics. One shipped model teaches you more than five notebooks.
5. GenAI for data work. Learn embeddings, build a small retrieval system over documents you understand, and above all learn to check LLM output. Make a test set of fifty questions, score the answers, and write down the failures. This is where "almost right" bites hardest.
6. One domain, properly. Pick banking, retail, pharma, logistics or whatever you know, and learn its metrics, its data and its rules. A data scientist who understands how a loan book or a clinical trial works is worth far more than one who knows a fifth algorithm.
What I would postpone
Deep learning theory from first principles can wait until you have a problem that needs it. Kaggle leaderboard chasing teaches tricks that rarely matter at work. Learning R and Python together splits your attention in the first year; pick Python. And skip the big-data tooling until a job puts terabytes in front of you.
I would also skip collecting certificates. Hiring managers have seen hundreds of them. What they rarely see is a candidate who can show a deployed model, the test set behind it and a short note on where it fails.
What makes a fresher's portfolio believable in 2026?
Because AI can produce a tidy notebook in minutes, a tidy notebook proves very little now. Here is what I would want to see on a CV.
A project on messy, real data that you cleaned yourself, with a short write-up of what was wrong with it. A model that runs somewhere other than your laptop, with a link. An evaluation section that reports where the model fails as well as its best score. And one piece of writing, a blog post or a README, that a non-technical manager could follow.
If you can only do one, do the deployed model with the honest evaluation. It answers the two questions every interviewer is really asking: can you finish things, and can you be trusted with a number?
Who this is not for
Data science is a poor fit if you want a quick, certain salary jump in six months. The numbers above show that the large jumps come after two or three years of real work, and the first job may pay less than you hoped.
It is also a poor fit if you dislike ambiguity. Much of the job is working out what the question should be, finding that the data cannot answer it, and saying so to someone senior.
And if you enjoy building software more than questioning numbers, look hard at software or ML engineering instead. Those are excellent careers, and many people discover too late that they liked the coding part of data science much more than the statistics part.
What to do this week
- Write ten SQL queries against a public dataset, including at least two with window functions. If that feels hard, you have found your starting point.
- Open three real data job postings in your city, one analyst, one junior data scientist, one data scientist, and list what each asks for that you cannot yet show.
- Pick one project you could deploy in a month, and write down how you will measure whether it is wrong.
- Check what your current skills are likely to pay before you spend money on anything.
For that last step we built a free data science salary calculator. You choose a role, experience band, city and the skills you already have, and it shows a directional range and how much each skill moves it. It is a rule-of-thumb model, not a survey, so read it next to the chart above. Its Data Scientist setting prices the full title, so if you are a fresher, run it for Data Analyst as well and compare. The most useful thing it shows is which skills move the number, which is a good way to decide what to learn next. If you want a personal roadmap, there is an optional form for a short call with our counsellors.
A question for the comments: if you work in data in India, what did your first role actually ask of you in month one: SQL and reporting, or modelling? I suspect SQL wins for most people, and I would like to know if I'm wrong.
Pankit Kumar is a Senior Data Scientist at Parexel with ten years in data science and AI, much of it building production systems in regulated pharma and clinical settings. He leads the data science and forward deployed engineering programmes at NeuraPath Academy in Noida.
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