Use Case Discovery & Feasibility Study
We test business ideas against your data and ROI potential, so you invest only in problems that models can actually solve.
- Use case shortlist
- Feasibility report
Data & Analytics
Data Science Consulting Services
Data scientists frame the business question, build and validate statistical models, and design A/B tests, handing you results and reproducible code your team can rerun and extend.
- Experiment design
- Statistical modelling
- Python & R notebooks
- Model validation
- Years of delivery
- 8+
- Years of delivery
- Projects shipped
- 350+
- Projects shipped
- Clients across US, UK, AU & SG
- 200+
- Clients across US, UK, AU & SG
What’s included
Everything You Need to Put Data Science Behind Your Decisions
From problem framing to validated models and experiments, we help you apply statistics where it changes real business outcomes.
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Data Audit & Feature Engineering
We assess data quality and build the variables models need, turning raw records into signals about customers and operations.
- Data audit
- Feature set
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A/B Testing & Experiment Design
We design statistically sound experiments for pricing, product and marketing, so you ship changes based on evidence.
- Test plan
- Sample size analysis
- Results readout
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Statistical Modelling & Hypothesis Testing
We apply regression, segmentation and causal methods to explain what drives your results, not just what happened.
- Model documentation
- Analysis notebooks
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Machine Learning Prototype Development
We build and benchmark candidate models on your data so you see measurable accuracy before committing to production.
- Working prototype
- Benchmark report
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Model Validation & Bias Review
Independent testing checks accuracy, stability and fairness, so your leaders can trust the results they present.
- Validation report
- Bias assessment
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Model Handover & Production Readiness
We package models with code, documentation and deployment guidance so your engineers can run them without us.
- Reproducible code
- Handover guide
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Data Science Team Coaching & Mentoring
We mentor your analysts and hires on methods, tooling and review practices, building lasting in-house capability.
- Code reviews
- Coaching sessions
Need something specific?
Why Kramate
Why Business Owners Choose Kramate for Data Science Advice
We test your idea on a small scale first, so you spend big money only on what holds up.
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We Test Your Idea First
Instead of
spending months on an idea that cannot work
With Kramate
We check your data against your idea first, and we tell you honestly if it will not work.
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Maths Explained Like a Chat
Instead of: results nobody outside the lab understands
With Kramate: We explain each result in everyday words, and show how sure we are about it.
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A Second Check on Every Result
Instead of: trusting a model nobody else has checked
With Kramate: Before you see any result, someone who did not build it checks it for mistakes and unfairness.
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Handover Your Team Can Run
Instead of: work that only its author can repeat
With Kramate: We write down how it works so your own people can run it again without us.
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Pay for a Step, Then Decide
Instead of: signing up for a huge project blind
With Kramate: We split the work into small stages, so you can stop or carry on after each one.
Our approach
How We Run Data Science Consulting, Step by Step
We test your ideas against real data before you spend on a full build.
6 clear steps 6 deliverables you keep
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Idea Feasibility
We check whether your question can be answered with the data you have, and say so honestly.
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Data Preparation
We clean your records, fill gaps and shape the details a model needs to learn from.
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Fair Test Planning
We plan fair tests, such as A/B tests, so results come from evidence and not hunches.
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Model Prototype
We build a small working model and try several approaches to see which fits your data best.
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Bias & Accuracy Review
We test the model on data it has not seen and look for unfair or odd results.
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Handover & Coaching
We hand over the model, notes and code, then coach your team on keeping it running.
Tools & technologies
Technologies We Use for Data Science
We use open, well-supported libraries so your team can maintain every model we hand over.
- Python: Readable language for business logic, data and automation.
- R: Statistical language widely used for modelling and data analysis.
- Jupyter: Notebooks for exploring data and sharing findings.
- Anaconda: Manages Python and R environments so analysis runs the same everywhere.
- pandas: Cleans and reshapes business data ready for modelling.
- NumPy: Fast numerical computing underneath most data science work.
- SciPy: Python library for statistics and scientific calculations.
- statsmodels: Python library for regression, hypothesis tests and time-series models.
- scikit-learn: Classic machine learning for prediction and classification.
- XGBoost: Fast gradient boosting for accurate predictions on tabular data.
- TensorFlow: Framework for training and deploying machine learning models.
- PyTorch: Framework for building and training neural networks.
- MLflow: Tracks experiments and manages model versions.
- DVC: Versions datasets and models alongside your code.
- Git: Version control that keeps every change to code and models traceable.
- Docker: Packages software so it runs the same everywhere.
Expert guide
Data Science Consulting Services: A Complete Guide for Businesses
Data science consulting services apply statistics, experimentation and machine learning to a specific business question, such as which customers will leave, how much stock to hold, or whether a new checkout flow really performs better. An engagement ends with a tested result and something your team can run, not a notebook that only its author understands.
The hardest part is rarely the algorithm. It is framing the question, finding data that actually reflects it, and proving the answer holds on new cases.
This guide organizes a project into gates, shows how to design experiments and validate models, explains what operating a model involves, and covers privacy and regional points for the USA, UK, Australia and Singapore.
What data science consulting services cover and when to use them
Data science helps when a decision depends on patterns that are too large or subtle for manual review, or when you need to separate cause from coincidence. Typical work includes forecasting, classification, segmentation, recommendation, anomaly detection and controlled experiments.
It is not the right tool when the real problem is missing or unreliable data, or when a simple rule would do. A good consultant will say so early, and may recommend data engineering first, or a broader analytics consulting review.
People often confuse three roles. Analysts explain what happened, data engineers make data available and dependable, and data scientists build and test models that estimate or predict.
Gate one: frame the problem before touching data
Write the question as a decision: if the model says X, who does what differently? A churn score is only worthwhile if someone will call, discount or fix something for flagged customers, and that action has a cost.
- Target and unit: what exactly is predicted, for whom, and over what time horizon.
- Baseline: the current method, even if it is a rule of thumb. A model must beat it.
- Success measure: a business measure and a technical one, agreed before building.
- Data check: do the needed fields exist for enough past cases, with labels you trust?
- Stop rule: the evidence that would make the team pause or end the project.
Projects with no baseline and no action attached are a common reason data science disappoints.
Gate two: experiments and model validation
Two kinds of testing matter, and they answer different questions. Controlled experiments, such as A/B tests, tell you whether a change causes a result. Model validation tells you whether a prediction holds on cases it has not seen.
| Aspect | Controlled experiment | Model validation |
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| Question | Did the change cause the effect? | Will predictions hold on new data? |
| Key discipline | Random assignment, a planned sample size and a fixed stopping rule | Hold-out data kept apart from training, split by time where order matters |
| Common mistake | Stopping early when results look good | Leakage: information from the future slipping into training |
| Output | Estimated effect with uncertainty | Error measures against the baseline |
Insist on uncertainty ranges, not single numbers. For large datasets, tools such as Apache Spark (opens in a new tab), with its machine learning library, can train and evaluate models across a cluster.
Gate three: from notebook to something you can run
A model creates value only when it runs on fresh data and its output reaches a person or system. That requires engineering beyond the experiment: versioned code, reproducible training, a stable data feed, tracking of model versions and a way to roll back.
Platforms such as Databricks (opens in a new tab) provide managed environments for notebooks, training runs and model tracking, and similar capabilities exist in other clouds. Pick the environment your team will actually operate.
Decide the delivery form up front: a scheduled batch score written to a table, a service called in real time, or a one-time analysis with a report. For forward-looking forecasts that run continuously, see predictive analytics services.
Gate four: monitoring and drift
Models age. Customer behavior, prices and data sources change, so a model that was accurate at launch can slowly degrade. This is called drift.
- Track input data quality, for example with checks written in Great Expectations (opens in a new tab).
- Compare predictions with real outcomes once they are known.
- Set a threshold that triggers review or retraining.
- Keep an owner and a retraining runbook.
Responsible use, privacy and fairness
Models trained on personal data inherit its biases and its privacy obligations. Check whether the model uses fields that act as proxies for protected characteristics, test results across groups, and keep a person involved in decisions that affect people materially, such as credit, hiring or care. A formal program of data governance consulting can set those rules.
Document what the model is for, what data it used and its known limits. The NIST Privacy Framework (opens in a new tab) offers a structure for managing privacy risk, and good data management practice as described by DAMA International (opens in a new tab) supports the data foundations underneath. Avoid unsupported claims about what a model can do, since the FTC (opens in a new tab) expects marketing and data practices to be truthful.
Industry use cases
- Retail: demand forecasting by store and week to reduce both stockouts and overstock, validated against last season.
- Banking: anomaly detection on transactions, with analysts reviewing flagged cases.
- Healthcare operations: appointment no-show models used to schedule reminders, not to deny care.
- Subscription software: an A/B test of a new onboarding flow measured on activation.
These are illustrative examples, not Kramate client results.
Regional considerations
Training data often contains personal information, and automated decisions attract extra scrutiny. This is a starting map; confirm with legal counsel.
- UK: the ICO’s UK GDPR guidance (opens in a new tab) addresses lawful basis, transparency and rules on solely automated decisions.
- USA: state laws such as the California Consumer Privacy Act (opens in a new tab) and sector rules like HIPAA apply depending on the data.
- Australia: the Australian Privacy Principles (opens in a new tab) cover collection, use and cross-border disclosure.
- Singapore: the PDPC (opens in a new tab) oversees the Personal Data Protection Act, which sets consent and purpose rules.
Teams spread across time zones should hold experiment reviews at times when the people who own the decision can attend.
Cost factors and planning
Uncertainty is built into data science consulting services, so plan the budget in stages. A short feasibility phase tests whether the data can support the question before a larger build is funded.
- Data readiness: cleaning and labeling can outweigh modeling.
- Number of models and how often they are retrained.
- Whether work is a one-time analysis or an operated product.
- Compute for training and for serving predictions.
- Integration with the systems where predictions are used.
Fixed-price fits a clearly bounded feasibility study. A dedicated team fits continuing programs of experiments, and hourly work suits expert review of an existing model.
How to choose among data science consulting services
- Do they ask about the decision and baseline before proposing a model?
- Will they explain validation in plain language and show uncertainty?
- Can they deliver code you can run, with documentation and a handover?
- Do they say when not to use machine learning?
- Is there a plan for monitoring and retraining after launch?
How Kramate delivers data science
We sign an NDA before detailed discussions, and a free consultation is followed by an estimate within 24 hours. Each project has a dedicated project manager, daily updates on Slack or Teams, and a progress review each two-week round, which fits the gate-based approach above. All intellectual property, including code and models, passes to you. Details are in our delivery approach.
Frequently asked questions
When does a business actually need data science?
When a decision depends on patterns too large or subtle for manual analysis, or when you must separate cause from coincidence. If data is incomplete or a simple rule solves the problem, fix the data or use the rule first.
How do you validate a machine learning model?
Hold back data the model never saw, split by time when order matters, and compare errors against a simple baseline. Check performance across customer groups, look for leakage, and report uncertainty instead of one headline figure.
How do you design a valid A/B test?
Define the single metric and the minimum effect worth detecting, calculate a sample size, randomize assignment, and run for a planned period without peeking and stopping early. Then report the effect with its uncertainty.
Why do data science projects fail?
Common causes are unclear decisions, no baseline, poor data, leakage that inflates results, and no plan to run the model afterward. Short feasibility phases with explicit stop criteria reduce these risks.
What is model drift?
Drift is the gradual loss of accuracy as real-world data changes after training. Prevent surprises by monitoring inputs and outcomes, setting review thresholds and assigning an owner who can retrain the model.
How are data scientists different from analysts and data engineers?
Analysts explain past performance, data engineers build the pipelines that supply dependable data, and data scientists build statistical or machine learning models and experiments. Larger projects usually need all three roles working together from the start.
Sources and further reading
- Apache Spark Documentation (opens in a new tab)Apache Software Foundation
- Databricks Documentation (opens in a new tab)Databricks
- Great Expectations Documentation (opens in a new tab)Great Expectations
- NIST Privacy Framework (opens in a new tab)NIST
- DAMA International (opens in a new tab)DAMA International
- FTC Privacy and Security (opens in a new tab)FTC
- UK GDPR guidance and resources (opens in a new tab)UK ICO
- California Consumer Privacy Act (CCPA) (opens in a new tab)California Attorney General
- Australian Privacy Principles (opens in a new tab)OAIC
- Personal Data Protection Commission Singapore (opens in a new tab)PDPC
FAQ
Frequently asked questions
Cost depends on how well-defined the question is, how much usable data exists and how much experimentation is needed. Model complexity, explainability needs and deployment into your systems add effort. A short feasibility study costs less than a full build. We give a no-obligation estimate within 24 hours.
We frame the question, check data, explore, build and test models, then hand over or deploy. Timelines vary because data quality is unknown until we explore it, and experiments can need extra rounds. We share findings at each stage so you can stop or redirect early.
You do. Kramate signs an NDA before detailed discussions, and the models, notebooks, code, experiment records and reports transfer to you. Your data is not used to train anything for other clients. Everything is documented so your team can reproduce results.
We work with your analysts on problem framing and with your engineers on deployment. Code goes into your repositories, follows your review process and runs on your infrastructure. We explain methods in plain terms so your team can maintain and question the results.
Statistical modelling is better when you need clear explanations, have limited data, or must justify decisions to regulators or managers. Machine learning helps with large, complex data and prediction accuracy. We pick the simplest method that answers your question.
You need historical data tied to the outcome you care about, a clear definition of that outcome and someone who understands how the data was collected. A few months of clean records can be enough to test feasibility. We tell you early if data is too thin.
We test models on data they have not seen, compare them with simple baselines and check for bias and drift. We explain which inputs drive results. Where possible, we run a controlled experiment, so you see real-world effects before wider rollout.
Taking new projects
Let’s talk about your Data Science Consulting Services needs
Tell us about your project and get a free consultation and estimate within 24 hours.
- We reply within 4 business hours A real consultant reads your brief — no bot, no queue.
- 30-minute discovery call We dig into goals, constraints and budget. NDA signed on request.
- Proposal in 24 hours Scope, timeline, team and price — clear enough to decide on.
NDA on request You own the IP No obligation Free estimate