ScienceSoft vs EPAM Systems: full comparison for 2026
Quick verdict
ScienceSoft (4.0/5) edges ahead of EPAM Systems (3.9/5) overall. ScienceSoft is the better choice for regulated industries hiring experienced data scientists. EPAM Systems is the stronger option for global enterprises with large, compliance-heavy AI programs. The right choice depends on your project size, budget, and required tech stack.
ScienceSoft vs EPAM Systems: head-to-head summary
| Criterion | ScienceSoft | EPAM Systems |
|---|---|---|
| Founded | 1989 | 1993 |
| HQ | McKinney, Texas, USA | Newtown, Pennsylvania, USA |
| Team size | 750+ | 62,850 |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Primary differentiator | Senior data scientists with a published hiring timeline | Scale, compliance maturity and vendor certifications |
| Pricing model | Time and materials; rates sent with CVs | Enterprise time and materials; dedicated teams; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, R, Azure ML | Claude, OpenAI, Gemini |
| Industries served | Healthcare, Manufacturing, Fintech, Retail | Fintech, Healthcare, Retail, Manufacturing, Travel |
ScienceSoft vs EPAM Systems: 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).
EPAM Systems
EPAM Systems was founded in 1993 and is headquartered in Newtown, Pennsylvania, with about 62,850 employees as of June 30, 2026, of whom roughly 56,650 work in delivery. It reports more than 5,700 Claude-certified engineers and set a target of 10,000, along with thousands of OpenAI- and Gemini-certified specialists. The company is targeting $600 million in AI-native services revenue for 2026. Its model is enterprise delivery, so individual staff augmentation usually sits inside a larger program.
Services and capabilities: ScienceSoft vs EPAM Systems
| Capability | ScienceSoft | EPAM Systems |
|---|---|---|
| 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 EPAM Systems
| Framework / platform | ScienceSoft | EPAM Systems |
|---|---|---|
| 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 | ✓ | ✓ |
| Azure ML | ✓ | ✓ |
| Databricks | N/A | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: ScienceSoft vs EPAM Systems
| Criterion | ScienceSoft | EPAM Systems |
|---|---|---|
| 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 EPAM Systems
| Dimension | ScienceSoft | EPAM Systems |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, Manufacturing, Fintech | Fintech, Healthcare, Retail |
| Best use cases | Adding a senior data scientist to a healthcare analytics team, Staffing a manufacturing predictive-maintenance project | Staffing a multi-team GenAI program at a global bank, Adding certified Claude engineers to an enterprise AI platform |
| Typical project type | Full-time dedicated engineers | Dedicated team |
ScienceSoft vs EPAM Systems: 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 |
| EPAM Systems | |
|---|---|
| + | Largest bench on this list, with security and compliance processes to match |
| + | Thousands of engineers certified on major model platforms |
| + | Can staff any role an AI program needs |
| - | Built for enterprise programs; a single-engineer request is a poor fit |
| - | Highest overhead and slowest procurement on this list |
| - | Rates not published |
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 EPAM Systems?
A typical fit: staffing a multi-team GenAI program at a global bank.
Scale, compliance maturity and vendor certifications. Minimum engagement is not publicly disclosed. Works best with clients in Fintech, Healthcare, Retail, Manufacturing, Travel.
Decision matrix: ScienceSoft vs EPAM Systems
| 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 EPAM Systems (Not disclosed) |
| You need specialist depth in a specific vertical | EPAM Systems |
| 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 EPAM Systems
| Use case | ScienceSoft fit | EPAM Systems fit | Winner |
|---|---|---|---|
| Adding a senior data scientist to a healthcare analytics team | Strong | Strong | Both equally |
| Staffing a manufacturing predictive-maintenance project | Strong | Strong | Both equally |
| Staffing a multi-team GenAI program at a global bank | Strong | Strong | Both equally |
| Adding certified Claude engineers to an enterprise AI platform | Strong | Strong | Both equally |
Verdict: ScienceSoft vs EPAM Systems
ScienceSoft (4.0/5) is the stronger overall choice for most AI Staffing projects. Senior data scientists with a published hiring timeline.
EPAM Systems (3.9/5) is worth a look if you need adding certified Claude engineers to an enterprise AI platform. If your situation matches that, EPAM Systems is a competitive option.
Related comparisons
ScienceSoft vs EPAM Systems FAQ
Is ScienceSoft better than EPAM Systems?
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. EPAM Systems's strongest advantage: largest bench on this list, with security and compliance processes to match.
How do ScienceSoft and EPAM Systems differ in pricing?
ScienceSoft uses time and materials; rates sent with cvs pricing. EPAM Systems uses enterprise time and materials; dedicated teams; 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 EPAM Systems?
EPAM Systems 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 EPAM Systems?
ScienceSoft's primary differentiator is: senior data scientists with a published hiring timeline. EPAM Systems's primary differentiator is: Scale, compliance maturity and vendor certifications. They also differ in team size (750+ vs 62,850), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Healthcare, Manufacturing vs Fintech, Healthcare).
Verify all details directly with each agency before making a decision.