ScienceSoft vs DataArt: full comparison for 2026
Quick verdict
ScienceSoft (4.0/5) edges ahead of DataArt (4.0/5) overall. ScienceSoft is the better choice for regulated industries hiring experienced data scientists. DataArt is the stronger option for financial and travel firms needing long-lived dedicated teams. The right choice depends on your project size, budget, and required tech stack.
ScienceSoft vs DataArt: head-to-head summary
| Criterion | ScienceSoft | DataArt |
|---|---|---|
| Founded | 1989 | 1997 |
| HQ | McKinney, Texas, USA | New York, USA |
| Team size | 750+ | 5,000–6,000 |
| Rating | 4.0 / 5 | 4.0 / 5 |
| Primary differentiator | Senior data scientists with a published hiring timeline | Dedicated development centers with nearly 30 years of history |
| Pricing model | Time and materials; rates sent with CVs | Dedicated development center; time and materials; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, R, Azure ML | Python, Azure ML, AWS |
| Industries served | Healthcare, Manufacturing, Fintech, Retail | Fintech, Travel, Healthcare, Media |
ScienceSoft vs DataArt: overview
ScienceSoft
ScienceSoft dates its IT work to 1989 and is headquartered in McKinney, Texas, with representative offices in the UAE, Saudi Arabia, Europe and Mexico. Its staff-augmentation pool covers more than 750 professionals, including data scientists with 7–20 years of experience. The company says it sends CVs with rates within 24 hours, arranges interviews in two to four days and has people starting within one to two weeks (per company website; independently unverifiable).
DataArt
DataArt was founded in New York in 1997 by Eugene Goland and now employs between 5,000 and 6,000 people across more than 40 locations. Its Dedicated Development Center model staffs a team that works on one client's project only. An AI/ML group is actively hiring, with recent roles including a lead AI/ML engineer for an HR copilot built for a French SaaS client.
Services and capabilities: ScienceSoft vs DataArt
| Capability | ScienceSoft | DataArt |
|---|---|---|
| LLM / GenAI engineers | ✗ | ✓ |
| MLOps & deployment | ✗ | ✗ |
| Computer vision | ✗ | ✗ |
| Data engineering | ✓ | ✓ |
| AI agent development | ✗ | ✗ |
| Fractional / part-time experts | ✗ | ✗ |
| Risk-free trial period | ✗ | ✗ |
| Nearshore time-zone overlap | ✗ | ✗ |
Tech stack comparison: ScienceSoft vs DataArt
| Framework / platform | ScienceSoft | DataArt |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | ✓ |
| AWS SageMaker | ✓ | N/A |
| Azure ML | ✓ | ✓ |
| Databricks | N/A | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | N/A | ✓ |
Pricing comparison: ScienceSoft vs DataArt
| Criterion | ScienceSoft | DataArt |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | Dedicated team, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: ScienceSoft vs DataArt
| Dimension | ScienceSoft | DataArt |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, Manufacturing, Fintech | Fintech, Travel, Healthcare |
| Best use cases | Adding a senior data scientist to a healthcare analytics team, Staffing a manufacturing predictive-maintenance project | Setting up a long-term dedicated team that includes ML engineers, Adding an LLM engineer to a SaaS copilot project |
| Typical project type | Full-time dedicated engineers | Dedicated team |
ScienceSoft vs DataArt: pros and cons
| ScienceSoft | |
|---|---|
| + | Rates arrive with the CVs, before any sales calls |
| + | Long history in healthcare and manufacturing IT |
| + | Experienced data scientists rather than junior ML hires |
| - | AI is one of many service lines |
| - | Smaller bench than the large nearshore firms |
| - | Headcount figures differ between the company's own pages |
| DataArt | |
|---|---|
| + | Long-running dedicated teams with low churn |
| + | Strong presence in finance and travel |
| + | Wide location choice |
| - | Built for multi-year centers more than quick single hires |
| - | AI/ML group is still growing |
| - | Enterprise pricing |
Who should choose ScienceSoft?
A typical fit: adding a senior data scientist to a healthcare analytics team.
Senior data scientists with a published hiring timeline. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Manufacturing, Fintech, Retail.
Who should choose DataArt?
A typical fit: setting up a long-term dedicated team that includes ML engineers.
Dedicated development centers with nearly 30 years of history. Minimum engagement is not publicly disclosed. Works best with clients in Fintech, Travel, Healthcare, Media.
Decision matrix: ScienceSoft vs DataArt
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Both offer fixed-price models |
| You need a large dedicated team for an ongoing programme | ScienceSoft |
| Your budget is at the lower end | Compare: ScienceSoft (Not disclosed) vs DataArt (Not disclosed) |
| You need specialist depth in a specific vertical | ScienceSoft |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: ScienceSoft vs DataArt
| Use case | ScienceSoft fit | DataArt fit | Winner |
|---|---|---|---|
| Adding a senior data scientist to a healthcare analytics team | Strong | Strong | Both equally |
| Staffing a manufacturing predictive-maintenance project | Strong | Limited | ScienceSoft |
| Setting up a long-term dedicated team that includes ML engineers | Limited | Strong | DataArt |
| Adding an LLM engineer to a SaaS copilot project | Strong | Strong | Both equally |
Verdict: ScienceSoft vs DataArt
ScienceSoft (4.0/5) is the stronger overall choice for most AI Staffing projects. Senior data scientists with a published hiring timeline.
DataArt (4.0/5) is worth a look if you need adding an LLM engineer to a SaaS copilot project. If your situation matches that, DataArt is a competitive option.
Related comparisons
ScienceSoft vs DataArt FAQ
Is ScienceSoft better than DataArt?
ScienceSoft (4.0/5) scores higher overall, but "better" depends on your use case. ScienceSoft's strongest advantage: rates arrive with the CVs, before any sales calls. DataArt's strongest advantage: long-running dedicated teams with low churn.
How do ScienceSoft and DataArt differ in pricing?
ScienceSoft uses time and materials; rates sent with cvs pricing. DataArt uses dedicated development center; time and materials; 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: ScienceSoft or DataArt?
DataArt is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each agency before shortlisting.
What are the main differences between ScienceSoft and DataArt?
ScienceSoft's primary differentiator is: senior data scientists with a published hiring timeline. DataArt's primary differentiator is: dedicated development centers with nearly 30 years of history. They also differ in team size (750+ vs 5,000–6,000), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Healthcare, Manufacturing vs Fintech, Travel).
Verify all details directly with each agency before making a decision.