Pre-vetted Data Scientist talent, fully managed delivery, structurally outcome-based pricing via Delivery Units. Onboarded in days — not months. No hiring overhead.
A Data Scientist pod from AiDOOS is a pre-assembled execution unit — vetted talent, a delivery manager, and the tooling to ship outcomes against your roadmap. We handle vetting, onboarding, governance, and reporting. You review shipped work against milestones.
Data Scientists at AiDOOS combine statistical modeling, machine learning, and business analysis to deliver insights and decision-support tools. Specialists cover the full ML lifecycle — problem framing, data exploration, feature engineering, model selection, evaluation, and deployment-readiness. Tooling expertise includes Python (pandas, scikit-learn, PyTorch), SQL across modern warehouses, and ML platforms (Databricks, Sagemaker, Vertex AI).
Typical seniority: 5+ years with strong applied-statistics background. Data Scientists fit pods doing analytics deep-dives, ML proof-of-concept work, and the modeling layer of larger ML-engineering engagements. AiDOOS distinguishes Data Science (analysis and modeling) from ML Engineering (production deployment) — the two roles complement rather than substitute.
AiDOOS maintains a pre-vetted bench. Kickoff happens after scope alignment — not after a 60–90 day hiring funnel.
Every pod ships with a delivery manager, code-review SLAs, integration with your GitHub / Jira / Monday, and milestone reporting. Outcomes are auditable.
Add or release Data Scientist talent without long-term commitments. Delivery Unit (DU) pricing means you only pay for shipped, accepted work.
Pods are composed for the engagement. Data Scientists on AiDOOS pods commonly work across these technology stacks — pick the stack-specific page for engagement-fit details.
Pods include Data Scientists with prior sector experience. Each industry page covers compliance posture and common engagement types.
Tell us the outcomes you want shipped. We'll come back with a pod composition, milestone plan, and a pricing proposal — usually within 48 hours.