Question
60-min call to nail down the exact question and the decision it would inform.
Data
ML / Data Analytics Sprint.
One business question, answered properly — model, dashboard, or pipeline plus the written reasoning behind the answer.
One question answered with data — model, dashboard, or pipeline — with a written report.
What you get.
Every deliverable below is included in the scoped engagement — no upsell at handoff.
One question, properly answeredWe focus on one specific business question — churn prediction, segment analysis, attribution, forecast — and answer it well rather than answering ten badly.
Model, dashboard, or pipelineThe artefact that fits the question: a trained model, a dashboard, or a data pipeline.
Written reasoningA report covering the data, the method, the assumptions, the limits, and what we'd do next with more time.
Reproducible codeA repository with notebooks or scripts that anyone on your team can re-run.
Stakeholder readoutA 30-minute walkthrough of the result with your team and any executives who need it.
Next-step recommendationsA short list of what's worth doing next, ranked by expected impact.
How it works.
The same four-step flow we use across every engagement, scoped to this gig.
Data audit
We assess your data and surface any gaps that would block the analysis.
Analyse + iterate
Mid-sprint checkpoint with a draft result for review.
Deliver
Report, artefact, and a recorded walkthrough.
Tools we use.
The stack we default to for ml and data analytics sprint work. Always open to fitting yours.
Python
Pandas
scikit-learn
DuckDB
SQL
Metabase
dbt
Why work with us on this.
Three reasons clients pick Hepha Works for ml and data analytics sprint.
Senior practitioners only
The person who scopes the work is the person who delivers it. No invisible subcontractors, no junior handoffs.
Written scope, fixed price
You see a written scope and a number before any work starts. No timesheet surprises, no scope-creep arguments.
Honest read on outcomes
We won't say it'll work if we don't think it will. If the gig isn't right for your situation, we'll tell you that on the call.
Frequently asked.
The questions we get most before kicking off ml and data analytics sprint engagements.
What if my data is messy?
Most data is. We'll spend the first day on data quality, document what we found, and tell you what's needed before the question can be answered.
Can you use my warehouse?
Yes — BigQuery, Snowflake, Redshift, Postgres, or files. Read-only credentials are enough for the sprint.
Do you build production models?
This sprint is exploratory. Production deployment is a separate engagement once the question is answered.
Can you train my team?
A handoff session and the written report are included. Deeper training is a separate engagement.
What's the typical answer look like?
Sometimes a confident yes/no, often a probability range with caveats. We won't claim certainty we don't have.
Related engagements.
Other gigs that pair well with ML / Data Analytics Sprint.
Ready to start?
Send a brief and we'll come back with a written scope and a number within 1–2 business days.