Data science and machine learning consulting firms, ranked by public evidence

The CRISP-DM process model, a long-standing reference for data mining projects, defines six phases: business understanding, data understanding, data preparation, modeling, evaluation and deployment. It notes that the sequence is not rigid and that projects move back and forth between phases. The table maps each phase to what a consultant should hand over.

Google's MLOps guidance makes the operations point: the challenge is not building a model but building an integrated machine learning system and operating it continuously, and models can decay in more ways than conventional software as data changes. Sculley and colleagues at Google called the maintenance burden hidden technical debt in 2015. Breck and colleagues' ML Test Score lists 28 tests across data, model development, infrastructure and monitoring, which makes a practical acceptance checklist for production readiness. Model cards (Mitchell et al., 2019) are short documents that record a model's intended use and how it performs under different conditions.

For language-model applications such as retrieval, assistants and agents, see AI consulting firms, our guide to a generative AI proof of concept and our guide to RAG implementation.

When not to start

• There is no data or there are no labels. RAND lists missing data as one of five root causes of failed AI projects.
• No one owns the decision the model would change.
• A rule, a query or a report would answer the question. RAND also lists problems too difficult for AI as a root cause, so let the feasibility phase conclude that the answer is no.

RAND recommends committing a product team to a problem for at least a year, so treat the figures below as the cost of reaching and passing the first release, not the cost of owning a model.

The scenarios use GSA CALC+ role medians for data scientists ($169 an hour, 1,488 records), machine learning and AI engineers ($187, 230 records) and data engineers ($149, 838 records). They are fully burdened ceiling prices on U.S. federal contracts, not commercial quotes, and commercial rates for the same roles are often higher. The hours are our assumptions.

The last column reprices the same hours at the 25th and 75th percentile GSA rates for each role. Cloud compute, data labeling and monitoring tools are extra and priced by their vendors. See our MLOps consulting guide for the operating side and our AI consulting pricing guide for rate context.

The shape of the first phase matters as much as its price. STX Next describes a Quick Prediction Sprint: a fixed-price, three-week proof of concept that compares two to three model types against a baseline and ends with a go or no-go recommendation (firm-reported, checked October 2026). Whatever the vendor, look for the same structure: a short, bounded first phase with a decision at the end.

Ask which of the 28 ML Test Score tests the firm will implement and which it expects your team to own. Check that the people who will do the work have run a model in production, not only built one. Monitoring should appear as a deliverable: Protiviti's advanced analytics and AI page, for example, lists model management and model maintenance, meaning monitoring of model health, degradation and data changes, as explicit service lines (checked October 2026).

RAND's 2024 report on why AI projects fail is based on those 65 interviews. Each root cause gives you a question to put to a firm before you sign.

Sculley and colleagues add machine-learning-specific maintenance risks that proposals rarely price: entanglement between features, hidden feedback loops, undeclared consumers of a model's output, data dependencies and changes in the external world. A firm that has run models in production will usually raise some of them unprompted.

Governance is not optional for regulated uses. Article 10 of the EU AI Act sets data governance and management requirements for the training, validation and testing data of high-risk AI systems.