Quantiphi vs Data Science UA: full comparison for 2026
Quick verdict
Quantiphi (4.3/5) edges ahead of Data Science UA (3.8/5) overall. Quantiphi is the better choice for enterprises that need many AI roles filled at once by one AI-only supplier. Data Science UA is the stronger option for companies that want to hire Ukrainian ML engineers directly, with an outstaffing option meanwhile. The right choice depends on your project size, budget, and required tech stack.
Quantiphi vs Data Science UA: head-to-head summary
| Criterion | Quantiphi | Data Science UA |
|---|---|---|
| Founded | 2013 | 2016 |
| HQ | Marlborough, Massachusetts, USA | Kyiv, Ukraine (legal HQ London) |
| Team size | 3,000–4,000+ | 50–200 |
| Rating | 4.3 / 5 | 3.8 / 5 |
| Primary differentiator | The biggest AI-only bench here, sold through a named staffing program with AWS | A large AI community and conference series that feeds its recruiting |
| Pricing model | Elastic Staffing billed per specialist; consulting quoted separately; rates on request | Recruiting fee per hire; outstaffing billed monthly; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, PyTorch, TensorFlow |
| Industries served | Healthcare, Financial services, Energy, Retail, Media | Technology, Fintech, Healthcare, Retail, Gaming |
Quantiphi vs Data Science UA: overview
Quantiphi
Quantiphi, based in Marlborough, Massachusetts and founded in 2013, is the largest company on this page that works only on AI and data, with directory estimates between 3,000 and more than 4,000 people. Its Elastic Staffing program, built with AWS, places generative AI and ML specialists into client teams. That scale is the reason it ranks here: no other AI-only supplier can staff ML, MLOps, data and LLM roles in parallel. Google Cloud named it 2025 AI Partner of the Year for North America. The cost is attention, since staffing is one product inside a large consulting business.
Data Science UA
Data Science UA began in 2016 as a data science conference in Kyiv, founded by Aleksandra Boguslavskaya, and grew into a recruiting, outstaffing and AI consulting business. Recruiting is a core line, and it says hiring averages two to four weeks. Its community of AI engineers in Ukraine and beyond, quoted at 10,000 to 30,000 depending on the source, gives it reach that general agencies lack. The screening is recruiter-led, though, so the technical depth of each shortlist depends on how well you brief them and on your own interviews.
Services and capabilities: Quantiphi vs Data Science UA
| Capability | Quantiphi | Data Science UA |
|---|---|---|
| ML engineers | ✓ | ✓ |
| LLM / GenAI engineers | ✓ | ✗ |
| AI agent developers | ✗ | ✗ |
| MLOps engineers | ✓ | ✗ |
| Computer vision engineers | ✓ | ✓ |
| NLP engineers | ✗ | ✗ |
| Data engineers | ✓ | ✓ |
| Engineer-led technical screen | ✗ | ✗ |
| Fractional / part-time experts | ✗ | ✗ |
| Trial before commitment | ✗ | ✗ |
| Nearshore time-zone overlap | ✗ | ✗ |
| Direct hire option | ✗ | ✓ |
Tech stack comparison: Quantiphi vs Data Science UA
| Framework / platform | Quantiphi | Data Science UA |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Google Cloud | ✓ | ✓ |
| Databricks | ✓ | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: Quantiphi vs Data Science UA
| Criterion | Quantiphi | Data Science UA |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Dedicated engineer, Dedicated team, Project delivery | Direct hire, Dedicated engineer, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Quantiphi vs Data Science UA
| Dimension | Quantiphi | Data Science UA |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, Financial services, Energy | Technology, Fintech, Healthcare |
| Best use cases | Staffing eight GenAI specialists into an enterprise program, Adding Vertex AI or SageMaker engineers for a cloud ML migration | Hiring a permanent computer-vision engineer in Ukraine, Building an AI R&D centre in Europe for a U.S. product company |
| Typical project type | Dedicated engineer | Direct hire |
Quantiphi vs Data Science UA: pros and cons
| Quantiphi | |
|---|---|
| + | Can staff several AI specialties in parallel, which no other AI-only firm here can |
| + | Top partner tiers with Google Cloud and AWS help on cloud-specific ML roles |
| + | A named staffing product makes procurement simpler |
| - | Requests for one or two engineers compete with large consulting programs |
| - | Rates appear only after scoping |
| - | Headcount estimates vary widely between sources |
| Data Science UA | |
|---|---|
| + | Recruiters specialise in AI and data, so briefs are understood |
| + | Direct hire and outstaffing both available |
| + | Wide reach in the Ukrainian AI community |
| - | Screening is done by recruiters, not engineers |
| - | Size and headquarters differ across directories |
| - | Wartime conditions need a continuity plan |
Who should choose Quantiphi?
A typical fit: staffing eight GenAI specialists into an enterprise program.
The biggest AI-only bench here, sold through a named staffing program with AWS. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Financial services, Energy, Retail, Media.
Who should choose Data Science UA?
A typical fit: hiring a permanent computer-vision engineer in Ukraine.
A large AI community and conference series that feeds its recruiting. Minimum engagement is not publicly disclosed. Works best with clients in Technology, Fintech, Healthcare, Retail, Gaming.
Decision matrix: Quantiphi vs Data Science UA
| Your situation | Recommended choice |
|---|---|
| You want a working engineer, not a recruiter, to run the technical screen | Neither documents an engineer-led screen; run your own technical interview |
| You need one specialist for a few days a week | Neither advertises part-time experts; ask about reduced hours |
| You need several engineers working as one team | Both; Quantiphi rates higher overall |
| You want to test an engineer before committing | Neither publishes a trial; negotiate a short first term |
| Your budget is at the lower end | Compare: Quantiphi (Not published) vs Data Science UA (Not published) |
| Your team works U.S. hours | Neither lists Latin American engineers; confirm overlap hours in the contract |
| You may want to hire the engineer permanently later | Data Science UA |
Use case fit: Quantiphi vs Data Science UA
| Use case | Quantiphi fit | Data Science UA fit | Winner |
|---|---|---|---|
| Staffing eight GenAI specialists into an enterprise program | Strong | Strong | Both equally |
| Adding Vertex AI or SageMaker engineers for a cloud ML migration | Strong | Limited | Quantiphi |
| Hiring a permanent computer-vision engineer in Ukraine | Limited | Strong | Data Science UA |
| Building an AI R&D centre in Europe for a U.S. product company | Limited | Strong | Data Science UA |
Verdict: Quantiphi vs Data Science UA
Quantiphi (4.3/5) is the stronger overall choice for most AI Engineer Staffing projects. The biggest AI-only bench here, sold through a named staffing program with AWS.
Data Science UA (3.8/5) is worth a look if you need building an AI R&D centre in Europe for a U.S. product company. If your situation matches that, Data Science UA is a competitive option.
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Quantiphi vs Data Science UA FAQ
Is Quantiphi better than Data Science UA?
Quantiphi (4.3/5) scores higher overall, but "better" depends on your use case. Quantiphi's strongest advantage: can staff several AI specialties in parallel, which no other AI-only firm here can. Data Science UA's strongest advantage: recruiters specialise in AI and data, so briefs are understood.
How do Quantiphi and Data Science UA differ in pricing?
Quantiphi uses elastic staffing billed per specialist; consulting quoted separately; rates on request pricing. Data Science UA uses recruiting fee per hire; outstaffing billed monthly; rates on request pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Quantiphi or Data Science UA?
Quantiphi is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each company before shortlisting.
What are the main differences between Quantiphi and Data Science UA?
Quantiphi's primary differentiator is: the biggest AI-only bench here, sold through a named staffing program with AWS. Data Science UA's primary differentiator is: a large AI community and conference series that feeds its recruiting. They also differ in team size (3,000–4,000+ vs 50–200), minimum engagement (Not published vs Not published), and primary industries served (Healthcare, Financial services vs Technology, Fintech).
Verify all details directly with each company before making a decision.