Short answer first: in 2026, Indian companies hiring junior data scientists are looking for solid SQL and Python fundamentals, real project experience they can actually explain (not just list), comfort working alongside AI tools rather than being replaced by them, and enough business sense to explain why a number matters, not just what the number is. A certificate alone doesn’t do it anymore. A degree alone doesn’t do it either. What’s changed in the last two years is how much weight “proof of work” now carries compared to credentials.
If that sounds like the bar has gone up, it has. Here’s what’s actually driving that, and what it means for you if you’re trying to land one of these roles this year.
The Market Is Bigger Than Ever — and Also Pickier Than Ever
These two things sound contradictory, but they’re both true at once, and understanding why matters more than either fact on its own.
On the “bigger” side: NASSCOM’s own research, State of Data Science & AI Skills in India, puts India’s projected demand for data science and AI professionals at over 1 million by 2026, and flags a demand-supply disparity of 60–73% specifically for ML engineers, data scientists, DevOps engineers, and data architects. That’s not a minor shortage — it means for every handful of open roles in this band, there’s roughly one qualified candidate for every two or three positions.
So on paper, this should be a candidate’s market. And in some ways it is — Glassdoor’s India salary data puts the average data scientist salary at ₹14.75 LPA, with senior and staff-level roles climbing well beyond that.
But here’s the “pickier” side: that demand is concentrated at the experienced end. Payscale’s 2026 data puts the average entry-level junior data scientist salary in India at roughly ₹4.8 LPA — a wide gap from the overall average, and a sign that junior roles are priced (and screened) very differently from the market as a whole. Companies aren’t short on applicants for junior roles. They’re short on junior applicants who can actually do the job on day one.
Why “Entry-Level” Doesn’t Mean What It Used To
This is the part most career advice skips, and it’s the most important context for understanding what companies actually expect from you right now.
Storyboard18’s reporting found that entry-level IT roles in India have already declined by 20–25%, largely because AI now handles a lot of the repetitive, rules-based work that used to be a fresher’s job — cleaning data, writing boilerplate code, running standard reports. That squeeze is showing up in fresher hiring data too: TeamLease EdTech’s Career Outlook Report for the second half of 2026, reported directly by PeopleMatters, found that while overall fresher hiring intent across India rose to 75% for July–December 2026, IT-sector fresher hiring intent specifically fell to 76% from 81% in the first half of the year — one of the few sectors actually pulling back even as the national picture improved.
None of this means junior data science roles are disappearing. It means the definition of “junior” has shifted upward. Companies aren’t asking freshers to already be experts — they’re asking freshers to already be useful, on real problems, without months of hand-holding first.
There’s actually reassuring data underneath this too. PwC’s 2026 Global AI Jobs Barometer, which analyzed over a billion job postings across six continents, found that the most AI-exposed junior roles are seven times more likely to demand traditionally senior human capabilities — judgment, leadership, communication — compared to the least AI-exposed junior roles. The roles aren’t gone. They’ve been redesigned around what AI can’t do well yet.
This tracks with what the World Economic Forum’s Future of Jobs Report 2025 found globally as well: analytical thinking remains the single most sought-after core skill, considered essential by seven in ten employers, even as AI and big data top the list of fastest-growing technical skills. Companies aren’t choosing between hiring for judgment or hiring for AI fluency — they increasingly want both in the same candidate.
Globally, graduates themselves seem to be adjusting to this reality. CFA Institute’s 2026 Graduate Outlook Survey found that while 59% of graduates worldwide still see AI and automation as a potential barrier to landing their desired role, that’s down from 67% in 2025 — a sign of growing (if cautious) acceptance. Seventy-two percent said they felt confident applying AI tools professionally, and 95% viewed upskilling and additional qualifications as important in today’s job market, well above what formal degrees alone are seen to deliver.
So What Are Indian Companies Actually Screening For?
Based on current hiring patterns, industry data, and what’s showing up consistently in job descriptions across IT services, product companies, and analytics consultancies, here’s what’s actually getting junior candidates shortlisted right now.
- Real fluency in SQL and Python — not “I completed a course”
This is table stakes, but the bar for “fluency” has risen. Companies aren’t just checking whether you know the syntax — they’re checking whether you can write a query or a script to solve a problem you haven’t seen before, under mild time pressure. Course completion doesn’t demonstrate this. Solving unfamiliar problems does.
- Comfort working with AI tools, not fear of them
This flips a lot of people’s instincts. Some candidates worry that leaning on AI tools during a task will make them look less skilled. In practice, the opposite is increasingly true — employers want to see that you know how to use AI to move faster, and just as importantly, that you know how to check its output rather than trust it blindly. Not knowing how to work alongside these tools is now more of a red flag than using them.
- A portfolio you can actually talk through, not just show
A GitHub link full of tutorial-following projects doesn’t move the needle much anymore. What does: two or three projects where you made real decisions — why this model, why this feature, why this metric — and can explain the tradeoffs out loud in an interview. Depth on a few projects beats breadth across many.
- Business framing, not just technical output
A junior candidate who can say “this model predicts churn with 84% accuracy” is fine. A junior candidate who can say “this model flags at-risk customers early enough for the retention team to actually act, and here’s why that timing mattered” stands out. Companies are increasingly hiring for people who can connect a number to a decision, not just produce the number.
- Evidence you can learn fast, because the tools will keep changing
Nobody expects a fresher to know everything. What they’re checking for is a track record of picking things up quickly — a new library, a new tool, a new domain — because the tooling in this field turns over faster than almost any other. Interview questions increasingly probe for this directly: “tell me about something you had to learn on your own.”
- Basic domain awareness for the industry you’re applying to
A candidate applying to a fintech company who understands the difference between a false positive and a false negative in fraud detection, even at a basic level, reads as more hireable than one who only knows the general ML theory. You don’t need deep domain expertise as a fresher — but showing you’ve thought about how your skills apply to the industry you’re targeting goes a long way.
What This Means If You’re Job-Hunting Right Now
The honest takeaway from all of this: the path into a junior data science role in India hasn’t closed, but it has gotten more specific. Vague preparation — a general course, a stack of certificates, a passive understanding of machine learning theory — is a weaker bet in 2026 than it was even two years ago. Targeted preparation — real projects, tool fluency, the ability to explain your thinking — carries a lot more weight.
If you’re still deciding which direction to specialize in before you even start job-hunting, it’s worth reading our earlier post on choosing between Data Analyst and Data Science roles, since the skills employers screen for differ meaningfully between the two. And if you’re weighing a third path entirely, our breakdown of Data Analyst vs. Data Scientist vs. Data Engineer roles covers how these tracks actually differ in day-to-day work, not just job titles.
On the portfolio question specifically — since it’s consistently the biggest gap we see — our guide on data science projects that actually get you hired in 2026 is a good next read, built around exactly the kind of project depth employers are now screening for.
If you’re earlier in the decision — still weighing whether data science is worth pursuing at all given how much the market has shifted — we’ve answered that directly in Is Data Science a Good Career for Freshers in 2026? Short version: yes, but with the caveats covered in this post.
And if AI-tool fluency specifically feels like the gap for you, our piece on strategic upskilling in the AI era goes deeper into how to build that without needing a computer science background.
Coming back to the field after a break brings a slightly different set of questions, since some of these expectations (like AI-tool fluency) may have shifted since you were last job-hunting — we’ve written specifically about restarting a career after a break without starting from scratch, which pairs well with everything above.
Frequently Asked Questions
- What skills do Indian companies look for in a junior data scientist in 2026?
Strong SQL and Python fundamentals, applied machine learning knowledge, comfort working with AI tools rather than avoiding them, and the ability to explain a project’s business impact — not just its technical output. Real, explainable project work matters more than the number of certificates on a resume. - Is a data science degree required to get hired in India?
Not strictly. A relevant degree helps, but most companies are now screening more heavily for demonstrated skills — real projects, technical assessments, and interview performance — than for the degree itself. That said, foundational knowledge in statistics and programming still needs to come from somewhere, whether that’s a degree or a structured course with strong project components. - Why is it harder for freshers to get data science jobs in India now?
It’s not that fewer roles exist — NASSCOM projects demand for data science and AI professionals in India will exceed 1 million by 2026. What’s changed is that AI now handles much of the repetitive work freshers used to be hired to do, so companies expect junior candidates to add value beyond routine tasks from day one. - Do Indian companies expect junior data scientists to know AI and GenAI tools?
Increasingly, yes. It’s less about knowing a specific tool and more about demonstrating that you can use AI to work faster while still critically evaluating its output. Candidates who show zero familiarity with AI tools now stand out for the wrong reasons. - How many projects should a junior data scientist have in their portfolio?
Quality matters far more than quantity. Two or three well-understood, end-to-end projects that you can explain in depth — including the decisions and tradeoffs behind them — are more valuable than ten shallow, tutorial-based projects. - What’s the average salary for a junior data scientist in India in 2026?
Entry-level junior data scientist salaries in India average around ₹4.8 LPA according to Payscale, though this varies significantly by city, company size, and the strength of a candidate’s project portfolio and technical assessment performance.
The Bottom Line
The junior data scientist hiring bar in India hasn’t just gone up — it’s gotten more specific about what it’s actually measuring. Companies aren’t looking for freshers who know everything. They’re looking for freshers who can be useful quickly, explain their thinking clearly, and work comfortably alongside the AI tools that are now part of the job. That’s a learnable, buildable set of expectations — it just takes more intentional preparation than it used to.
At Data Brio Academy, our data science programs are built directly around this shift — real project work, AI-tool fluency built into the curriculum, and mentorship focused on helping you explain your work as confidently as you built it. If you want to talk through what a job-ready portfolio should look like for your background, get in touch with our team.