Data science consulting services from engineers who ship the model.
Outside specialists who turn a business problem into a data problem, build and validate the model that solves it, and get it running where it makes money - in production, not in a slide deck. That is what our data science consulting services are for, and the good firms also tell you when you don't need a model at all.
BroutonLab has shipped ML since 2017. The people who write the roadmap are the same people who build the pilot, at the price they quoted, with the architecture they proposed. Rates and scope are published below, not negotiated on a call.
What our data science consulting services cover.
Data readiness audit
What your data can support today and what it can't yet: coverage, labels, leakage, access. Roughly half of the projects we scope change shape at this step, which is cheaper than changing shape in month four.
Machine learning and predictive models
Forecasting, scoring, classification, document extraction - the data science services most products actually need, and the data scientist services most proposals stop at, built with a baseline first and an evaluation set you agreed on before training started.
Data engineering and pipelines
Ingestion, validation, feature code shared between training and serving, retraining gates - the data science services that decide whether a model survives month six. The part most data science consultancy proposals leave to "your team"; ours includes it.
Ranking and matching engines
Search, recommendations, candidate-to-role matching over millions of records. Our sourcing agent for a staffing platform runs at roughly a thousand requests per second on a single 16-core server.
Analytics that feed a model
Dashboards and cohort analysis when they are inputs to a decision system. If you only need BI, a data analytics consulting firm will do it for less, and we will tell you so.
Data strategy and governance
Data science strategy consulting in the narrow sense: which of your ten ideas has data, a measurable target, and a payback under twelve months. Documented data flows, access control, self-hosted deployment where data can't leave your infrastructure.
Four shapes of data science outsourcing. The shape matters more than the vendor.
Fixed-fee audit
First engagement, second opinion, a stalled project
2-3 weeks, fixed fee
Fixed-price pilot
First production system, proving one use case
$30-50K, 2-4 months
Dedicated team
Ongoing product work, several models, a roadmap
from $15K/mo
Retainer
Roadmap reviews, vendor selection, board questions
$5-15K/mo
Hourly data scientist services cover only the middle of the arc; a data science professional services contract that ends at a notebook costs the same as one that ends in production and returns nothing. Our bias, stated plainly: for a founder with one clear use case, start with the fixed-price pilot - it is the shape of data science consulting services that caps your downside and forces honest scoping.
The deck is not the deliverable.
A data science consulting firm that sells strategy and leaves before the first line of code has a predictable failure mode: a roadmap no one can implement at the quoted budget. A data science consulting company that only sells hours has the opposite one: a project that passes through three engineers in a year, each handover dropping context. Our data science consulting services are a third shape - the same three to five engineers from audit to production, code in your repository from day one, handover written into the contract. It is easy to be visionary when you will never see the codebase again. We will, so we aren't.
Audit. Roadmap. Then - only if the math holds - build.
- 01
Audit - 2 to 3 weeks
We read your data, your code, and your product plan; interview the team; and test what the data can really support. Fixed fee agreed upfront, no obligation to continue.
- 02
Roadmap with numbers
A written plan your engineers can execute with us or without us: prioritized use cases, model and pipeline choices with reasons, cost and timeline estimates, and the risks we would bet on going wrong first.
- 03
Pilot, if the math holds
When the roadmap says build, the same people who wrote it ship the first slice: a fixed-price pilot ($30-50K, 2-4 months) that ends with a model running in your infrastructure, monitored and retrainable.
The pipeline a model needs to survive production is described in our machine learning pipeline guide; the broader question of where AI fits your product at all belongs to AI consulting services, including machine learning consulting for problems LLMs are wrong for. Comparing firms? The data science consulting buyer's guide covers market rates, engagement models, and the nine questions to ask before you sign.
When to hire a data science consultancy, and when not to.
Talk to us when
- You have a real decision to improve - pricing, matching, fraud, forecasting - and data that might support it
- A model works in a notebook and nobody can say what it costs to run it every night
- You tried to hire a senior data scientist for six months and the first hire didn't work out
- You are comparing data science consulting firms and every proposal ends at the prototype
Don't hire us when
- The problem is solved off the shelf - transcription, basic OCR, sentiment on reviews. Buy the tool and spend the money on integration
- You have under a few thousand examples and no way to get more - a rules engine will beat a model, and you can maintain it
- Getting to your data means asking an engineer to run a query by hand - a data engineer is a better first hire than a consultant
Data science consulting services with production behind them.
AI sourcing agent
Candidate search over millions of profiles for a 200-recruiter staffing team: semantic taxonomies, Elasticsearch, about a thousand requests per second on one 16-core server.
Read the case →Sports fan video generation
Facial recognition and automated video assembly at stadium scale - a computer vision pipeline that had to work on game day, not in a demo.
Read the case →Questions founders ask a data science consultancy.
What do data science consulting services include?
The full arc of data science services, or any part of it: a data readiness audit, use-case prioritization, model development against an agreed evaluation set, the pipeline that feeds the model, deployment and monitoring, and training your team so the knowledge stays. If a proposal only lists the first three, you are buying a prototype - price it as one.
How much do data science consulting services cost?
Market hourly rates run $100-350, which is exactly why we don't bill by the hour. Our audit is a fixed fee scoped to 2-3 weeks; a production pilot is $30-50K fixed for 2-4 months; a dedicated team starts at $15K per month. The number is known before you commit to it, and it is the same number we publish on every page of this site.
What does a data science consultant do, day to day?
Scopes a business problem into a measurable prediction or decision task, audits the data, builds and validates models, and either hands the result to your team or ships it to production with you. The senior ones also tell you when a model is the wrong tool. Our buyer's guide to data science consulting covers the role, the rates, and the questions to ask before you sign.
Data science consulting or AI consulting - which do we need?
Data science consulting is the right door when the question is a model over your own data: forecasting, ranking, scoring, extraction. AI consulting is the right door when the question is where AI fits your product at all - LLMs, agents, build vs buy. Same engineers, same audit format; pick the page that matches your question and we will redirect you if we disagree.
Can we outsource data science entirely?
Yes, with one condition written into the contract: handover. Data science outsourcing fails when the model that shipped in month four is unmaintainable by month twelve because three engineers rotated through it. Our teams are the same people for the whole engagement, the code lives in your repository from day one, and your engineers pair with ours before we leave.
Do you work with a data science consulting firm's existing models?
Often. Whether they came from a data science consulting company or an in-house hire, we audit models and pipelines another team built - including ones nobody currently understands - and tell you what is salvageable, what to rewrite, and what each path costs. A messy working model is a better starting point than a clean idea.