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Graduate Data Scientist

₹5–13 LPA
Entry level · 0–1 years · Hybrid · Gig or permanent · Indicative CTC, India
In demandHiring at volume. One of the largest graduate intakes in the Indian market by number of openings, recruiting from a wide set of campuses — more doors, and more realistic odds, than the selective tracks.AI-exposedAI is changing what this role does day to day. The work is not disappearing, but what employers screen for is shifting — from producing output to verifying it and owning the decision.
Capability
StatisticsModellingValidity reasoning
Disposition & behaviour
ConscientiousnessScepticismExplaining to non-experts
Take the qualification testQualify once. Your verified profile goes to employers hiring for this role.

GoMeasure Platform runs GoMeasure Campus — a talent network for final-year students and new graduates, where what you can do is measured from real work rather than claimed on a CV.

We’re looking for Graduate Data Scientists to join this network ahead of the placement cycle. Frame a problem statistically, build a model and state honestly what it can and cannot support.

You’ll take one qualification test covering statistics, modelling, validity reasoning, build a verified profile, and go to the employers hiring into this track — typically ₹5–13 LPA at entry level.

Role summary

Frame a problem statistically, build a model and state honestly what it can and cannot support.

What you will do

In the first six months

  • Frame a business question as something statistically answerable
  • Build models, and spend far more time on the data that feeds them than on the model itself
  • Validate honestly — including when the result says your approach does not work
  • Explain a model to people who will act on it without understanding the maths

By twelve to eighteen months

  • Own a model in production and its performance over time
  • Say when a problem does not need a model, which is often
  • Shape what gets measured rather than only analysing what already is

Required skills

SkillWhat good looks like at entry level
StatisticsDistributions, inference and the limits of a sample. The gap between someone who can run a model and someone who knows whether to believe it.
PythonAnalysis and modelling to working standard. Near-universally screened in a coding round alongside SQL.
SQLAssumed, not optional. Most data science interviews include a SQL round regardless of how strong the modelling is.
Validity reasoningKnowing what your result can and cannot support. The most-valued and least-taught capability in the discipline.
CommunicationExplaining uncertainty to a decision-maker without either overclaiming or being useless.

Disposition & traits

Skills describe what someone can do when they try hardest. Disposition describes what they typically do — and over a first year, that second question predicts as much as the first. For this track the dispositions that matter most are:

  • Conscientiousness
  • Scepticism
  • Explaining to non-experts
How to read these. Higher is not automatically better — a disposition that helps in one role works against another. These are not a pass mark, not trainable in the way a skill is, and never reported on their own.

What the hiring bar looks like

Compiled by GoMeasure from publicly available accounts, October 2026.

Employer typeWhat they screen for
Product companies & GCCsAn online assessment covering programming and SQL, then live coding, a discussion of your own projects, and a case study. Typically four rounds over three to five weeks.
Analytics firmsAptitude, then SQL and statistics rounds, then a case interview on how you would approach an open problem.

Eligibility — who actually gets to apply

The most competitive track in this domain, and worth being realistic about. Many dedicated data science roles — particularly at global employers — ask for a master's or doctorate in a quantitative field, so a bachelor's-level route usually runs through analyst or engineering work first. Degree requirements commonly span engineering, statistics, mathematics, economics and physics.

Eligibility is set per drive and your placement cell’s notice is what actually applies on your campus. One trap worth knowing: a 6.0 CGPA is not always 60%. Where a university converts with (CGPA − 0.75) × 10, a 6.0 is 52.5% — below most employers’ floor. Check which formula yours uses before assuming you qualify.

And on the package: CTC is not take-home. A ₹4 LPA offer lands nearer ₹28,000–32,000 a month once provident fund, gratuity and tax come out, and offers with a large variable or joining-bonus component differ more again. Compare offers on fixed monthly pay, not on the headline. On-campus mass recruiters rarely move off a standard package; startups, GCCs and mid-size firms hiring off-campus often have some room.

Who should apply

Graduates whose degree and interests line up with the work above. Eligibility aside, employers in this track screen on demonstrated capability more than on which campus you attended.

This role is probably not for you if you want a defined problem and a clear answer. Most of this work is establishing whether the question can be answered at all.

Evaluation notice

Results are shared with the student and, with consent, with employers hiring into this track. Scores carry the evidence and the assessment date. Practice and assessed sessions are clearly distinguished before either begins.

What you get back

The outcome of qualifying is a report, not a pass mark. Three layers, each scored and reported separately — there is no single number, deliberately, because a composite hides the trade-off an employer actually needs to see.

01 · Capability

What you can do at your best

Role-specific skills and real work, placed on a proficiency level with the evidence attached.

StatisticsModellingValidity reasoning
02 · Disposition

What you typically do

How you tend to work, and the judgement you show in realistic situations. Not a pass mark, and higher is not automatically better.

ConscientiousnessScepticismExplaining to non-experts
03 · Alignment

What you are optimising for

What you want from work, and whether a role supplies it. Read as fit rather than quality.

Fit, not good or bad
Who sees it

Every layer carries its own score, its weighting and the date it was assessed — and you see the same report the employer does. The report is shared with employers hiring into this track, with your consent, and you can withdraw it. Individual employers are named on the drive itself, once that employer is participating.

Retaking the test

One qualification attempt per track, with a retake available after one to two months. The wait is deliberate: a retake a week later measures how well you remember the test, not whether anything changed. The gap is long enough for preparation against your reported gaps to actually show.

Get placed in this track

One qualification test puts you in the talent pool for this role, with a verified profile that employers can act on — instead of a CV that looks like every other CV in the stack.

Join the talent pool