ScienceSoft
US-headquartered IT veteran with deep healthcare and finance ML compliance expertise since 1989
Founded 1989 | McKinney, TX | 750+ employees
custom-mlml-consultingdata-engineeringmlopsai-strategy
What is ScienceSoft?
ScienceSoft is an IT services company founded in 1989 and headquartered in McKinney, TX, with 750+ employees. The firm's ML practice covers the full pipeline including data preprocessing, feature engineering, algorithm selection, and model training, with clear industry specialisations in healthcare and finance that include regulatory compliance expertise. ScienceSoft is noted for translating complex ML requirements into production systems that meet HIPAA, PCI-DSS, and SOC 2 standards.
ScienceSoft was founded in 1989 and is headquartered in McKinney, TX. The firm employs 750+ people and works primarily with clients in Healthcare & Life Sciences, Financial Services, Manufacturing & Industrial, Retail & E-commerce sectors. Its primary differentiator is: Over 35 years of regulated IT delivery — compliance-aligned ML architecture is a core competency, not an add-on.
ScienceSoft tech stack and services
PythonTensorFlowScikit-learnAzure MLAWS SageMakerRSQLPower BI
| Service area |
|---|
| Custom ML Development |
| ML Consulting |
| Data Engineering |
| MLOps & Deployment |
| AI Strategy |
ScienceSoft use cases
Short answer: ScienceSoft is best suited for Healthcare/finance orgs, HIPAA/PCI-DSS/SOC2-compliant ML.
| Use case |
|---|
| HIPAA-compliant predictive readmission model for healthcare system |
| PCI-DSS-aligned fraud detection ML pipeline for payment processor |
| SOC 2 compliant data science infrastructure for SaaS fintech platform |
| Manufacturing predictive maintenance with audit-ready model documentation |
| Custom ML model for retail demand forecasting with compliance data lineage |
ScienceSoft pricing
Short answer: ScienceSoft uses a fixed project, t&m pricing approach. Minimum engagement starts at $30K.
| Engagement model | Typical range | Best for |
|---|---|---|
| Fixed project | From $30K | Well-defined scope |
| Time & materials | Variable; depends on team size | Large programmes or team augmentation |
ScienceSoft does not publish a public rate card. Contact them directly via their website to get project-specific pricing.
ScienceSoft pros and cons
| Advantages | Things to consider |
|---|---|
| +35+ years of regulated IT delivery — compliance frameworks like HIPAA and PCI-DSS are deeply embedded | -Less generative AI and LLM depth than firms that built AI-native practices post-2020 |
| +Full ML pipeline coverage from data preprocessing through deployed model documentation | -Conservative delivery approach prioritises compliance over speed — not ideal for fast-moving experimental ML |
| +US HQ with McKinney TX base reduces offshore communication risk for North American clients | -Large portfolio breadth (IT services beyond ML) means ML is one of many practices, not the core product |
| +Industry specialisation in healthcare and finance provides vertical domain depth | |
| +Accessible $30K minimum for compliance-aware ML projects |
ScienceSoft vs alternatives
How ScienceSoft compares to the other top Machine Learning Development companies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Mid-market teams, specialist CV/time-series/LLM delivery. | Boutique ML depth, with access to the 25-year enterprise delivery experience of its parent company — a rare combination in the ML services market | 4.9 | Full comparison |
| LeewayHertz | Businesses, GenAI/LLM integration plus custom ML. | Among the earliest boutique firms to build a structured GenAI delivery framework — deep LLM orchestration and RAG pipeline experience | 4.1 | Full comparison |
| Scopic | Companies needing genuinely custom ML architectures. | Engineers custom ML architectures from the ground up — not fine-tuned wrappers — with 20 years of production delivery discipline | 4.6 | Full comparison |
| InData Labs | Complex ML problems, deep data-science expertise. | Boutique firm with a track record of solving atypical, high-complexity ML problems that generalist shops decline or under-deliver on | 4.6 | Full comparison |
| DATAFOREST | Mid-market companies, full ML pipeline ownership. | Structured MLaaS delivery model — one team owns data engineering, model development, and post-deployment monitoring end-to-end | 4.5 | Full comparison |
| Forte Group | Regulated finance/insurance firms, audit-ready ML governance. | ML delivery built for regulated environments — model risk governance, audit trails, and compliance-aligned architecture are built in, not bolted on | 4.5 | Full comparison |
| RTS Labs | High-growth US companies, production-accountable ML partner. | Small by choice, senior by design — every project is staffed with senior practitioners accountable for post-launch performance, not just the plan | 4.5 | Full comparison |
| Quantiphi | Enterprises, cloud-native ML, top-tier AWS/GCP credentials. | AWS Premier and four-time Google Cloud Partner of the Year — the highest independently verified cloud ML credentials in the market | 4.4 | Full comparison |
| N-iX | EU/US enterprises, large dedicated teams, competitive rates. | Scale and depth in one package — 2,000+ engineers with a mature ML practice and competitive EU delivery rates | 4.4 | Full comparison |
| Miquido | Product companies, ML/GenAI embedded, fast time-to-demo. | GenAI and mobile ML integration in one team — a rare combination for companies building AI-native products for end users | 4.4 | Full comparison |
| Algoscale | Fortune 500 and growth-stage companies, cloud data lakehouse... | 100+ production ML deployments on AWS, Azure, and Snowflake — proven at enterprise scale with multiple cloud stacks | 4.3 | Full comparison |
| STX Next | Python-stack product companies, tightly integrated ML plus MLOps. | Europe's largest Python shop — ML is embedded in full-stack Python systems with MLOps, not delivered as an isolated model | 4.3 | Full comparison |
| Intellias | Enterprises, AWS-native ML, validated CV/NLP/RAG results. | AWS AI Services Competency with verified production benchmarks — 10x TCO reduction in aerial imagery and sub-8-second NLP query latency | 4.3 | Full comparison |
| Simform | Manufacturing/logistics enterprises, cloud-native ML plus IoT. | AWS Premier Partner specialising in connecting physical IoT sensor data to cloud-based ML models for predictive maintenance | 4.2 | Full comparison |
| Oxagile | Healthcare/media/retail enterprises, cost-effective Eastern European ML. | Strong connected-care and healthcare AI track record combined with 40–60% cost advantage versus US equivalents | 4.2 | Full comparison |
| Softeq | Companies building AI for edge and embedded hardware. | Hardware-to-cloud ML engineering — a rare full-stack capability covering embedded device AI through cloud model serving | 4.1 | Full comparison |
| Aimprosoft | SMBs, AI consulting and custom ML at accessible... | Full-cycle AI delivery from consulting through implementation, optimised for SMB budgets and timelines | 4.1 | Full comparison |
| Uvik Software | Teams with an existing ML codebase, embedded senior... | Senior-only ML engineer staffing — embedded in your stack, working in your tools, without agency overhead | 4.1 | Full comparison |
| Ciklum | FinTech, Retail, Healthcare enterprises — AI product engineering... | 25+ AI products in production combined with 3,000+ global engineers — enterprise AI scale without the big-four overhead | 4.1 | Full comparison |
| Iflexion | ML newcomers, AI strategy and scoping first. | Consulting-first model ensures the ML problem is correctly defined before engineering investment begins | 4.0 | Full comparison |
| Itransition | EU-operating enterprises, GDPR/EU AI Act-compliant ML. | EU regulatory compliance depth for ML — GDPR-aligned data architecture and EU AI Act readiness built into delivery | 4.0 | Full comparison |
| DataToBiz | Startups taking an ML idea to market-ready delivery. | Product-oriented ML delivery — combines AI strategy with full-cycle engineering to produce launchable products, not just models | 4.0 | Full comparison |
| BairesDev | Enterprises and scale-ups, fast teams, US time-zone aligned. | Latin American engineering delivery with US time-zone alignment — faster team ramp than Asian offshore with significant rate advantage versus US onshore | 4.0 | Full comparison |
| Andersen Lab | Enterprises, large-scale ML, Fortune-500 references, EU footprint. | Named client references including Siemens, S&P Global, and Ryanair — enterprise ML track record at the highest scale | 4.0 | Full comparison |
| Intuz | SMBs, US-headquartered AI/ML at accessible rates. | 1,700+ delivered projects for SMBs — the broadest SMB ML delivery track record in this list | 3.9 | Full comparison |
| Tredence | Fortune 500 enterprises, AI analytics and supply-chain ML. | Large specialised analytics and AI firm — enterprise supply chain ML and CX analytics depth with Fortune 500 client delivery track record | 3.9 | Full comparison |
| Codiant | Budget-conscious orgs, end-to-end ML delivery. | Cost-efficient end-to-end ML delivery covering all phases — discovery, build, integration, and optimisation — in a single engagement | 3.9 | Full comparison |
| GlobalLogic (Hitachi) | Global enterprises, massive-scale MLOps, Hitachi-backed. | Hitachi Group backing with 27,000 engineers — the scale and compliance posture of a major industrial conglomerate applied to enterprise ML | 3.9 | Full comparison |
| EPAM Systems | Global enterprises, complex AI products, governance at scale. | AI-native engineering practice at 50,000-person scale — the broadest talent pool and delivery capacity of any firm on this list | 3.8 | Full comparison |
| Cognizant | Fortune 500 enterprises, multi-year AI transformation programmes. | One of the world's largest AI & Analytics practices — Fortune 500 industry vertical depth and compliance credentials at 350,000-person delivery scale | 3.8 | Full comparison |
| Accenture | Global enterprises, governed GenAI and agentic AI at... | Accenture's global AI practice applies consulting strategy, industry domain expertise, and engineering delivery at 700,000-person scale — designed exclusively for enterprise | 3.8 | Full comparison |
| DataRobot | Enterprise data-science teams, governed AutoML platform. | Platform-driven ML — DataRobot's AutoML engine and MLOps governance layer enable internal data science teams to build and manage models at scale without per-project custom development | 3.8 | Full comparison |
ScienceSoft FAQ
What is ScienceSoft?
ScienceSoft is an IT services company founded in 1989 and headquartered in McKinney, TX, with 750+ employees. The firm's ML practice covers the full pipeline including data preprocessing, feature engineering, algorithm selection, and model training, with clear industry specialisations in healthcare and finance that include regulatory compliance expertise. ScienceSoft is noted for translating complex ML requirements into production systems that meet HIPAA, PCI-DSS, and SOC 2 standards.
How much does ScienceSoft charge?
ScienceSoft uses fixed project, t&m pricing. Minimum engagement starts at $30K. A discovery call is required to get project-specific quotes.
What tech stack does ScienceSoft use?
ScienceSoft works with Python, TensorFlow, Scikit-learn, Azure ML, AWS SageMaker, R, SQL, Power BI. Primary industries served include Healthcare & Life Sciences, Financial Services, Manufacturing & Industrial, Retail & E-commerce.
Is ScienceSoft right for enterprise?
Healthcare/finance orgs, HIPAA/PCI-DSS/SOC2-compliant ML. 750+ team size. Key consideration: Less generative AI and LLM depth than firms that built AI-native practices post-2020.
What are the best ScienceSoft alternatives?
The best alternatives to ScienceSoft depend on your use case. Top options are:
- Tensorway: boutique ml depth, with access to the 25-year enterprise delivery experience of its parent company — a rare combination in the ml services market
- LeewayHertz: among the earliest boutique firms to build a structured genai delivery framework — deep llm orchestration and rag pipeline experience
- Scopic: engineers custom ml architectures from the ground up — not fine-tuned wrappers — with 20 years of production delivery discipline
Compare ScienceSoft with other Machine Learning Development companies
ScienceSoft vs Tensorway ScienceSoft vs LeewayHertz ScienceSoft vs Scopic ScienceSoft vs InData Labs ScienceSoft vs DATAFOREST ScienceSoft vs Forte Group ScienceSoft vs RTS Labs ScienceSoft vs Quantiphi ScienceSoft vs N-iX ScienceSoft vs Miquido ScienceSoft vs Algoscale ScienceSoft vs STX Next ScienceSoft vs Intellias ScienceSoft vs Simform ScienceSoft vs Oxagile ScienceSoft vs Softeq ScienceSoft vs Aimprosoft ScienceSoft vs Uvik Software ScienceSoft vs Ciklum ScienceSoft vs Iflexion ScienceSoft vs Itransition ScienceSoft vs DataToBiz ScienceSoft vs BairesDev ScienceSoft vs Andersen Lab ScienceSoft vs Intuz ScienceSoft vs Tredence ScienceSoft vs Codiant ScienceSoft vs GlobalLogic (Hitachi) ScienceSoft vs EPAM Systems ScienceSoft vs Cognizant ScienceSoft vs Accenture ScienceSoft vs DataRobot