Hire data scientists in India, powered by Mira AI.
Find data scientists and machine learning engineers whose models reached real decisions. Mira AI scores each application against the problem you are trying to solve.
Mira AI turns a sentence into a job description and a scorecard, then scores every applicant against it.
Build a career with a global team, on Indian payroll.
Trusted by 300+ Global Companies
The modelling work companies hire for most.
Open the one closest to the problem you have. Work outside this list is measured on the same scorecard.
Applied Machine Learning
scikit-learn, XGBoost, model training
Deep Learning
PyTorch, TensorFlow, GPU training
Statistics & Experimentation
A/B testing, causal inference, R
Forecasting & Time Series
Demand, revenue and capacity planning
Recommenders & Ranking
Personalisation, search ranking, embeddings
Model Deployment
Feature stores, serving, monitoring, drift
Any other modelling role
Optimisation, survival analysis, geospatial, bioinformatics and more. Describe it and Mira AI scores for it.
From a question to a model someone trusts.
Four steps, from framing the problem to a signed contract. Opening the role and screening what arrives carry no charge on the Base plan.
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01 Post
Post a role
- The question you want answered, the data you hold and how the answer gets used
- Mira AI turns it into a scorecard around the problem rather than a list of tools
- Data science salary band checked against payroll data before the role goes live
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02 Screen
Screen with AI
- Ranked on work that affected something real, not on competition placings
- Each position in the order carries its reasoning
- Referrals and agency submissions scored on the same scale
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03 Interview
Run interviews
- Notebooks, papers, published work and shipped models linked on the profile
- Book a case study round or a take-home modelling exercise in a click
- Notes, recordings and team scores kept against the role
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04 Decide
Decide together
- Wisemonk becomes the legal employer and issues the contract
- Payroll, provident fund, gratuity and tax withholding are handled every month
- An Indian entity is welcome here but never required
It reads the work, not the CV.
Mira AI reads every Machine Learning & Data Science application through the work that actually shipped: what the person owned, the constraints they worked inside, and the evidence they can show for both. Every applicant is measured against the same scorecard, in the same way, on the day they apply.
Proof beats a polished CV.
You describe the Machine Learning & Data Science role and what success looks like in it, and Mira AI ranks each applicant with the reasoning written out in sentences you can read and disagree with. The people who rise are the ones whose work backs up the claim. A first read, never the final word.
- Weighs work someone actually shipped above the tools listed on a CV
- Every ranking carries the why, including the near-misses
- Reads for judgement and communication alongside technical depth
- Reorders the shortlist as new Machine Learning & Data Science applicants arrive
Start from the question you need answered.
Data science roles grouped by the problem in front of you rather than by job title. A speciality not shown here can still be opened and scored the same way.
You want to understand what your data is telling you
This is analysis long before it is modelling. Someone who can frame the question properly will save you from building an elegant model of the wrong thing.
- Data Scientists
- Statisticians
- R Developers
- Python Developers
- Analytics Scientists
You want to predict a number or a category
Classic supervised learning, and the deepest modelling pool in India. Look for someone who has put a model into production rather than only into a notebook.
- Machine Learning Engineers
- ML Developers
- scikit-learn Developers
- XGBoost Engineers
- Predictive Modelling Engineers
You want to personalise or rank what users see
Recommenders are judged by live behaviour rather than offline scores. Experience running online experiments matters more here than the choice of model.
- Recommendation Engineers
- Ranking Engineers
- Personalisation Engineers
- ML Engineers
You are training models on images, audio or sensor data
Deep learning is a discipline of its own with GPU bills attached. The pool in India is smaller and distinctly more research-minded.
- Deep Learning Engineers
- PyTorch Developers
- TensorFlow Developers
- Computer Vision Engineers
The second model is where judgement shows.
Naming the decision you want owned filters far harder than naming a library or a model family in the advert.
Junior
0 to 2 years
Runs analyses and trains models on data someone else prepared. Needs review on validation, leakage and what a result genuinely supports.
Mid-level
3 to 5 years
Owns a problem end to end, from framing the question through to a model in production and the measurement around it. The deepest band in India.
Senior
6 to 9 years
Owns the modelling approach and the experiment design, decides what is worth modelling at all, and reviews the work of others. Notice is usually 60 to 90 days.
Lead / Principal
10 years and up
Sets data science direction, owns experimentation standards, and makes the business case for where modelling actually pays. A small pool.
Hire for the question you are asking.
Most data science hires go wrong at the brief rather than the interview. Start from the question and the right title usually picks itself.
| What you want to do | Who to hire | Typical tools | Pool in India |
|---|---|---|---|
| Understand why something happened | A data scientist, or an analyst if the question is narrow | SQL, Python, statistics | The deepest pool of the lot |
| Forecast a number | A data scientist with time series behind them | Prophet, statsmodels, gradient boosting | Deep, especially from retail and fintech |
| Classify or score records | A machine learning engineer | scikit-learn, XGBoost, PyTorch | Deep, though production depth varies widely |
| Rank or recommend items | An ML engineer who has worked on recommenders | Embeddings, ranking models, online experiments | Narrow, and concentrated in consumer companies |
| Model images, audio or sensors | A deep learning engineer | PyTorch, TensorFlow, GPU training | Smaller and more academic |
| Keep a model working in production | An ML platform or MLOps engineer | Pipelines, feature stores, drift monitoring | Narrow and rising in price |
| Prove a change caused a result | A data scientist with experiment design behind them | A/B testing, causal inference, R | Small, and worth paying for |
What belongs in a data science brief.
Choose the ones that matter for your problem and every applicant is measured against them as soon as they apply.
Validation discipline
Splits, leakage and cross-validation. A model that scores beautifully in a notebook and poorly in production has usually seen something it should not have.
Framing the question
Half of this job is deciding what to measure. Someone who reframes your question before touching the data is doing the work properly, not stalling.
Reaching for simple first
A logistic regression that ships beats a neural network that never does. Ask what the simplest approach they tried was, and why they moved past it.
Experiment design
Sample sizes, control groups and what counts as a real effect. Without this you cannot tell whether the model helped or the week was just good.
Production reality
Which features exist at inference time, how often the model retrains, and what drift looks like. Models tend to fail slowly and quietly.
Explaining uncertainty
A model matters only if a decision changes because of it. Look for someone who can hold a room of non-technical people and be honest about confidence.
Start with the tool. Add reach. Add people.
Every plan includes Mira. What changes is how far your roles travel and how much of the work you hand over.
Base
For a team running its own hiring and tired of doing it in spreadsheets.
- Full pipeline and candidate tracking
- Your own hosted careers page
- Mira in Slack, with monthly credits
- Unlimited open roles
Boost
For teams whose problem is candidate flow, not candidate tracking.
- Everything in Base
- Your roles listed on the Wisemonk talent community
- Mira on the strongest models available
- Uncapped screening and scheduling
- Salary benchmarks from live India payroll data
Bespoke
Some roles need a person on the phone. Our recruiters take over sourcing and interview coordination, working the pipeline Mira has already built — so you're paying for judgment and conversations, not for admin.
Contingent fee of 10%, 12.5% or 15% of first-year salary, set by role seniority. Under a talent agreement, billed only on a joined hire.
Frequently asked questions
What product and data leads ask before opening a first data science role in India.
How do I hire data scientists in India?
Open the role around the question you want answered rather than the algorithms you imagine using: what decision it feeds, what data you already hold, and who acts on the result. Mira AI drafts the job description and a scorecard built on that problem, the role reaches our candidate community, and every application is scored as it lands. You interview in Mira AI's order, and Wisemonk employs whoever you pick.
Do I need a data scientist or a machine learning engineer?
A data scientist works out what the data supports and whether a model is even the right answer. A machine learning engineer builds, trains and serves the model once that has been settled. If nobody has yet proven there is signal in your data, hire the scientist first. If you already know what to predict and need it running reliably, hire the engineer. Small teams often need one person who leans across both, which is a genuinely harder hire and priced accordingly.
How much does it cost to hire a data scientist in India?
Data science sits near the top of the Indian market, above back-end engineering and just below applied AI. Seniority drives it more than tools do, and deep learning specialists cost more than generalists because the pool is thinner. Bengaluru and Hyderabad price above the rest. When you open a role we check your band against our payroll data for data science and tell you before it goes live whether it will fill.
Should I hire a data scientist or an AI engineer?
They solve different problems despite the overlapping job adverts. If the work is building product features on top of existing models, that is an AI engineer. If the work is finding patterns in your own data, forecasting, experimenting or training a model specific to your business, that is data science. Teams that hire an AI engineer for a forecasting problem, or a data scientist to ship an LLM feature, usually end up hiring again.
How do I check whether someone's models ever reached production?
Ask what happened after the model was built. Anyone whose work actually shipped can tell you who used it, how it was monitored and what changed when it drifted, and most people cannot. Mira AI weighs models that fed a real decision above competition placings and research projects, and gives its reasoning for each ranking so you can see which side of that line an applicant sits on.
Do data scientists in India work US or UK hours?
Overlap with the UK and Europe is straightforward. A full US shift narrows the field, though less than it does for engineering roles, because data science work is often less tied to a standup and more to a weekly rhythm. Say what overlap the role needs and only candidates content with it will apply.
What does Kaggle experience actually tell me?
It tells you someone can model a clean dataset with a fixed target and a published metric, which is a genuine skill and not the whole job. What it does not tell you is whether they can frame a vague business question, cope with data that arrives broken, or decide that no model is needed. Treat a strong Kaggle record as evidence of technical ability and then screen hard for the judgement it does not test.
Do I need an entity in India to employ a data scientist?
No. Wisemonk acts as the legal employer, signs the employment contract and handles payroll along with provident fund, gratuity, ESI and income tax withholding. The work and the direction stay with you. Where your company already holds an Indian entity, the hire can be placed there instead, and that is decided at the offer.
What does senior mean for a data scientist in India?
Around six to nine years, owning the approach rather than the notebook: choosing the framing, designing the experiment, deciding what is not worth modelling, and reviewing other people's analysis. Titles inflate quickly in analytics-heavy service companies, so put the decisions you want owned into the scorecard instead of trusting the label.
Do I need a data engineer before a data scientist?
Very often, yes, and this is the most expensive ordering mistake in the category. A data scientist with no reliable pipelines spends most of the first year doing data engineering badly and expensively. If your data is scattered across systems, undocumented, or arriving broken, hire the data engineer first and the science becomes far quicker. If the data is already clean and queryable, go straight to the scientist.
Find your next data scientist in India.
Tell us the question and the data behind it. Mira AI writes the scorecard, puts every applicant in order against it, and Wisemonk handles the employment.
