Staff augmentation · India
Hire Data Scientists in India.Vetted shortlists for 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+
Average years in applied data science
Scientists with product or revenue decision support experience.
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
- Vetting process
- 4-step screen
- Engagement models
- Flexible
- Pricing range
- Custom bands
Role intake, sourcing, and technical screening through to a shortlist you can interview. Typical start within 2–4 weeks after you select a profile.
Stack and experience match, communication check, live technical review with a senior engineer, and reference checks before profiles reach you.
Dedicated augmentation, contract staffing, or contract-to-hire. Engineers work in your repositories, tools, and time zones, with IP assigned to you.
Monthly or hourly bands by seniority and stack, quoted before interviews so finance and engineering can align on budget.
Delivery metrics
- Average years in applied data science
- 5+
- Typical time to first experiment readout
- 3–6 wks
- Documented model assumptions
- 100%
- Training and scoring pipelines
- Reproducible
Scientists with product or revenue decision support experience.
Scoped once data access and the decision question are clear.
Power analysis and success metrics defined before launch.
Assumptions and failure modes included in every leadership readout.
Skills screened
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.
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.
