Hire Data Scientists Who Turn Your Data into Decisions

Our hiring process for data scientists includes a 60 to 90 minute live coding round in data science, followed by scenario based questions on practical data science applications, such as forecasting, customer segmentation and experiment design. Candidates who pass walk us through a recent production project and clear a third party background verification. New engineers then serve a probation period, and those who do not meet our bar do not continue with us. You interview shortlisted candidates yourself before anyone joins your team.

Yes, our data scientists deploy models into production systems, not just notebooks. For EComm Pulse, an ecommerce analytics SaaS platform, we built the data science and AI layer and deployed demand forecasting and pricing models that reduced stockouts by 30 percent across marketplaces. EComm Pulse is in production use with brands selling across Amazon, Shopify and direct channels, and its forecasts give restock recommendations with confidence ranges rather than point forecasts.

A data engineer builds the pipelines, databases and data warehouses that collect and organize data, while a data scientist analyzes that data by 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, and simple analytics and dashboards can add value even with modest datasets. Predictive models typically need hundreds to thousands of relevant records, and deep learning needs more. During discovery, we assess your data volume, quality and relevance to decide what is feasible now and what would benefit from more data collection.