You've decided to hire a data scientist to build forecasting models, churn prediction, or personalization systems. But before you bring them on, your data pipeline needs to be ready. If it's not, your data scientist will spend the first month just wrangling data instead of modeling — burning salary on infrastructure that should have been fixed earlier. This checklist makes sure your pipeline is solid before they arrive.
# The Data Pipeline Readiness Checklist
1. Your Data Sources Are Centralized or Mapped
Does your data scientist know where to find customer data? Product events? Financial data? If it's scattered across 10 different databases, Salesforce, and spreadsheets, the first month is exploration, not analysis. At minimum, have a documented map of where all your data lives.
2. You Have a Data Warehouse or Data Lake
A data scientist needs queryable data in one place, not scattered across production databases. If you don't have a Snowflake, BigQuery, or Redshift instance yet, budget and build it before hiring. A data scientist can help set it up, but starting without one wastes their time on infrastructure.
3. Your Data Is Reasonably Clean
This doesn't mean perfect. But you should have run basic validation: are user IDs consistent? Are timestamps in a known format? Are there massive null columns? Major data quality issues should be documented and either fixed or flagged upfront.
4. You Have Events or Behavioral Data Logged
If you're building predictive models, you need the underlying events — user signups, page views, purchases, churn events. If those aren't logged in your product or backend, there's no data scientist magic that can help. Ensure your product team has instrumentation in place.
5. Your Data Retention Policy Is Clear
How long do you keep data? 30 days? 1 year? Forever? A data scientist needs to know the window they're working with. If you delete data after 60 days but need 2 years of history for training, that's a problem worth solving before they start.
6. You've Identified the Target Variable or Outcome
For a churn model: what defines churn? Inactivity for 30 days? Cancelled subscription? For a forecast: what are you predicting? Revenue? Users? Be specific. Vague targets mean vague models.
7. You Have a Database Admin or Data Engineer in Place
A data scientist builds models, not databases. If your data warehouse needs ongoing maintenance, backups, access management, or optimization, you need someone handling that. Even part-time, this role prevents data scientists from burning time on infrastructure.
8. Your Data Access and Permissions Are Sorted
Can the data scientist query the warehouse? Can they access raw logs? Who approves access to sensitive customer data? Sort this out in advance. The first week shouldn't be waiting for permissions.
9. You've Defined Success Metrics for the Project
What does a successful model look like? 90% accuracy? 70%? Are you optimizing for precision or recall? Is latency important? Define success before the data scientist starts modeling, not after they've spent a month building something you don't want.
10. You Have Budget for Compute and Experimentation
Building ML models requires compute — cloud credits for training, GPUs, storage. Budget $200-1,000/month for small projects. A data scientist without resources is a data scientist who can't experiment.
# What If You're Missing Some of These?
If you're missing 1-2, it's recoverable — prioritize fixing the most critical gaps. Missing more than 3? Hire a data engineer first to build the pipeline, then hire the data scientist. It's faster than hiring both and having the scientist spin their wheels.
# The Order Matters
Ideally: data engineer first (builds the warehouse, cleans the data, sets up instrumentation), then data scientist (builds the models). Hiring them in the reverse order leaves your data scientist doing engineer work, which is expensive and frustrating for everyone.
# Frequently Asked Questions
Can a data scientist build the pipeline themselves?
Technically yes, but it's wasteful. A data scientist's time is expensive, and it's cheaper to have an engineer build infrastructure. The data scientist should focus on modeling.
How clean does the data need to be?
Clean enough that basic queries work and don't return weird results. You don't need perfection, but obvious issues should be fixed or documented.
What if we don't have a year of historical data?
That's OK for some projects (churn, forecast) and challenging for others (rare event prediction). It's a constraint worth understanding upfront.
Can we use a data scientist as a data engineer if we're small?
For the first month, maybe. But as your data needs grow, they'll need dedicated engineering support, or the data scientist becomes a bottleneck.
How much compute budget is realistic?
Start small: $200-300/month for experimentation. Grows to $500-1,500+/month if the model is in production and needs inference.
# Ready to Hire Your Data Scientist?
CompanyBench matches you with pre-vetted data scientists and data engineers. See our cost guides and team hiring guides on the Hire Data Scientists and Hire Data Engineer pages.
"See data scientist profiles at companybench.com/hire-talent/hire-data-scientists, and data engineer and pipeline talent — including data warehouse specialists — at companybench.com/hire-talent/hire-data-engineer. For a step-by-step plan to build your first data team, read companybench.com/blog/hire-data-engineering-team-checklist. For the full range of engagement models, start at companybench.com/hire-talent.
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