Hire Data Scientists,Perfectly matched to your team
Request pre-vetted data scientists in India. Shortlists in 2–3 weeks with transparent pricing for applied analytics and modeling work.
Request profilesimport pandas as pdfrom scipy import statscontrol = df.loc[df.variant == "control", "converted"]treatment = df.loc[df.variant == "treatment", "converted"]result = stats.ttest_ind(treatment, control, equal_var=False)lift = treatment.mean() - control.mean()print(f"lift={lift:.3%}, p-value={result.pvalue:.4f}")if result.pvalue < 0.05 and lift > 0: recommend("Ship treatment to 100% traffic")else: recommend("Keep control; rerun with larger sample")Core stack
- Python & scikit-learn
- Statistical modeling
- Experimentation
- Forecasting
- Reproducible notebooks
- SQL feature engineering
5+
Typical experience benchmark
Adjusted to the role scope, seniority, and ownership your team needs.
How we staff engagements
We support founders and engineering leaders who need capacity without a long permanent hire cycle. You get clear timelines, a defined vetting process, flexible engagement options, and pricing before interviews.
Placement speed
2–3 weeks
Role intake, sourcing, and technical screening through to a shortlist you can interview. Typical start within 2–4 weeks after you select a profile.
Vetting process
4-step screen
Stack and experience match, communication check, live technical review with a senior engineer, and reference checks before profiles reach you.
Engagement models
Flexible
Dedicated augmentation, contract staffing, or contract-to-hire. Engineers work in your repositories, tools, and time zones, with IP assigned to you.
Pricing range
Custom bands
Monthly or hourly bands by seniority and stack, quoted before interviews so finance and engineering can align on budget.
Staffing benchmarks
- Typical experience benchmark
- 5+
- Adjusted to the role scope, seniority, and ownership your team needs.
- Vetted shortlist target
- 1 week
- Typical after a calibrated brief; rare skills and feedback timing can extend the search.
- Candidate review
- 4 steps
- Experience, communication, technical review, and references before introduction.
- Replacement window
- 30 days
- Replacement search support if a placement is not the right fit after starting.
These are recruiting benchmarks, not guaranteed engineering outcomes. Search timing varies with seniority, skill scarcity, location, compensation, and interview feedback.
Tools we staff
Profiles are screened against your modeling and experiment workflow.
Python & scikit-learn
Applied models with evaluation and handoff.
Statistical modeling
Inference with assumptions stated clearly.
Experimentation
A/B design, power analysis, and readouts.
Forecasting
Forecasts with backtests for planning cycles.
Reproducible notebooks
Analysis that can be re-run by your team.
SQL feature engineering
Warehouse features with review notes.
Model cards & handoff
Limits, sources, and owners documented.
XGBoost & gradient boosting
Tabular models for business use cases.
SHAP & model explainability
Driver explanations for stakeholders.
Ready to strengthen your team?
Talk to Navastit about pre-vetted data scientists and a flexible engagement model.
Frequently asked questions
How long does it take to receive a shortlist?
Most roles are shortlisted in 2–3 weeks after intake. Timing depends on seniority, stack rarity, and how quickly feedback comes back on the first profiles.
How do you vet candidates before we interview?
We screen for stack and experience fit, run a communication check, complete a live technical review with a senior engineer, and verify references before profiles reach you.
What engagement models do you support?
Dedicated staff augmentation, contract staffing, and contract-to-hire. Engineers join your tools, repositories, and ceremonies. IP is assigned to your company.
What if the person is not a fit after starting?
We replace in up to 30 days at no extra sourcing fee. Tell us early so we can protect your timeline.
How does pricing work?
We quote monthly or hourly bands by seniority and stack before interviews. That keeps finance and engineering aligned on budget before you invest time in the process.
