Hire Data Scientists Who Turn Your Data into Decisions

Your developer works as an extension of your tech team, with direct communication in your channels and hours that overlap yours. Vetted candidates complete onboarding within 14 business days. If a developer underperforms, we replace them, and engagements run monthly with a 30 day cancellation notice.

We can match you with a vetted Data Scientist within a week. Our team includes pre-screened engineers with production experience in Data Science, so we skip the lengthy recruitment cycle and get straight to onboarding.

Three options: dedicated developers who work exclusively on your project, a managed team where we handle delivery end-to-end, or a project-based engagement with fixed scope and timeline. All models include a technical lead and regular progress updates.

Every data scientist is tested on real analysis work: a feature-engineering and model-selection exercise, an experiment-design and evaluation task (train/test discipline, metrics, leakage checks), a SQL and data-wrangling round, and a trial project. We also check how they communicate findings to non-technical stakeholders and work with a distributed team.

Yes. We share detailed profiles including relevant project experience, then arrange a technical interview so you can assess fit before committing. If the match is not right, we provide alternatives at no cost.

We offer a replacement guarantee. If the developer does not meet expectations within the first two weeks, we reassign and provide a replacement with no additional charges or delays to your project timeline.

Data engineers build the infrastructure: pipelines, databases, and data warehouses that collect and organize data. Data scientists analyze that data: building models, running experiments, and generating insights. Our team includes both skill sets, so we can build the full stack from data collection to production model deployment.

It depends on the problem. For simple analytics and dashboards, even modest datasets provide value. Predictive models typically need hundreds to thousands of relevant records. For deep learning, you need more. During discovery, we assess your data volume, quality, and relevance to determine what is feasible and what would benefit from additional data collection.