Hire data scientists and data engineers
A data science engineer combines data science (analysis, statistics, experimentation and forecasting) with the engineering to build reliable data pipelines. They help companies make decisions from data and prepare the clean, governed data that AI and ML systems depend on.
Skills we vet for
- Python, SQL and R
- pandas and Polars
- Statistics, A/B testing, causal inference, forecasting
- dbt, Airflow or Dagster, Spark, Kafka
- Snowflake, BigQuery, Databricks
- Power BI and Tableau
- Data quality and governance
What data science engineers build
- Analytics and BI foundations
- Experimentation programs
- Forecasting models
- Data warehouses and lakehouses
- Data pipelines that feed AI products
How we vet every data science engineer
Technical ability first, CV second. You only meet people who have already shown they can do the work.
Our vetting process- Initial screening of English, communication and remote-work readiness
- Practical technical assessment, reviewed by a panel of senior engineers
- Technical interview with a lead architect or lead engineer on system design and past production work
- Reference checks with past managers and senior colleagues
Hiring a data science engineer: questions, answered.
Should I hire a data scientist or a data engineer first?
If your data is scattered or unreliable, hire a data engineer first. If clean data already exists and you need answers, hire a data scientist.
Is data engineering needed for AI?
Yes. Most AI projects stall on data quality and access, not on models.
Other AI talent we place
Ready to hire your data science engineer?
Tell us what you're building. We'll help you find the people to move it forward.