Statistical investigation
Test a defined hypothesis with appropriate sampling, uncertainty and checks for misleading comparisons or selection effects.
Data Science Services
Data Science Services
Apply statistical analysis, experimentation, forecasting, machine learning, and decision modeling to business questions with explicit validation.
Inside the delivery
Test the hypothesis before operationalizing the model
Define the decision, target population, baseline and what evidence would support the hypothesis. The flow below shows the main delivery stages and the evidence produced at each step.
- 01
Problem formulation
Define the decision, target population, baseline and what evidence would support the hypothesis.
Output
Analytical protocol and success criteria
- 02
Dataset construction
Prepare features and splits while examining bias, missingness and target leakage.
Output
Reproducible dataset and feature definitions
- 03
Experiment and validation
Compare statistical or machine-learning approaches with uncertainty and relevant error slices.
Output
Experiment results and validation report
- 04
Decision handover
Explain limitations and specify how outputs could inform a workflow or further experiment.
Output
Model or analysis package and decision guidance
Controls across the workflow
- Baseline comparison
- Leakage checks
- Reproducible experiments
- Uncertainty reporting
Decisions that shape the scope
- Does a data science engagement include production model operations?
- It can establish a validated model or analysis. Serving infrastructure, monitoring, retraining and on-call ownership require a production engineering or MLOps scope.
- How much data do we need?
- Less than most teams assume for a narrow, well-defined question, and more than they hope for an open-ended one. The feasibility step answers it against your actual data rather than a rule of thumb, and produces a straight answer when the data will not support the question.
Before you commit
Is this the right engagement?
- What we need from you
- Decision context, historical data, target definitions, known sampling limits and the operational action a result would inform.
- How you accept the work
- Explain limitations and specify how outputs could inform a workflow or further experiment. Acceptance records the tested scope, unresolved issues and the owner's decision.
- Scope & alternatives
- It can establish a validated model or analysis. Serving infrastructure, monitoring, retraining and on-call ownership require a production engineering or MLOps scope.
Capabilities
Engineering scope and deliverables
Select the work that addresses your constraint. Responsibilities and acceptance criteria are agreed before delivery.
Feature and dataset design
Create reproducible analytical datasets with documented feature timing, missing-data treatment and validation boundaries.
Model experiments
Compare candidate approaches with a simple baseline and examine the errors that matter to the intended decision.
Decision communication
Deliver interpretable findings, assumptions and reproducible code, with a clear distinction between observed evidence and recommendations.
Integrations
Selected for your environment
Tools are chosen around your existing systems, access requirements and operating constraints.
Microsoft Azure
AWS
Databricks
Snowflake
dbt
Power BI and Tableau
Frequently asked questions
Questions to resolve before starting
01Does a data science engagement include production model operations?
Does a data science engagement include production model operations?
It can establish a validated model or analysis. Serving infrastructure, monitoring, retraining and on-call ownership require a production engineering or MLOps scope.
02How much data do we need?
How much data do we need?
Less than most teams assume for a narrow, well-defined question, and more than they hope for an open-ended one. The feasibility step answers it against your actual data rather than a rule of thumb, and produces a straight answer when the data will not support the question.
03How accurate will the model be?
How accurate will the model be?
Nobody can answer that before seeing the data, and a quoted figure up front is a warning sign. What we can commit to is measuring against your current process, so the comparison is to what you do today rather than to perfection.
04What happens when the model degrades?
What happens when the model degrades?
It will, because the world moves away from the training data. Monitoring and a retraining trigger are part of delivery, so degradation is detected against a baseline rather than noticed when someone complains about the output.
05What should we prepare for the first technical discussion?
What should we prepare for the first technical discussion?
Decision context, historical data, target definitions, known sampling limits and the operational action a result would inform.
06What evidence is available at handover?
What evidence is available at handover?
The agreed delivery includes model or analysis package and decision guidance. Explain limitations and specify how outputs could inform a workflow or further experiment.
07How is the engagement estimated?
How is the engagement estimated?
We review the available inputs before estimating: Decision context, historical data, target definitions, known sampling limits and the operational action a result would inform. The proposal identifies dependencies, review milestones and excluded work; the scope determines the schedule.
Start a conversation
Discuss your next technical step
Share your current situation and the constraint you need to resolve. We will use the discovery inputs above to define a practical scope for Data Science Services.