Applied data science creates value when experiments, forecasts, and models arrive with clear assumptions — not opaque notebooks leadership cannot act on. We place data scientists who design rigorous studies and document limits before decisions are made.
Our approach
We match scientists to the decision questions behind the hire: experimentation, forecasting, or applied modeling. Screening covers statistical rigor, reproducible workflow, and the ability to communicate tradeoffs to product and finance stakeholders.
Skills we staff
Profiles are matched to your modeling and experiment workflow.
- Python & scikit-learn — Applied models with evaluation and handoff
- Statistical modeling — Inference with assumptions stated clearly
- Experimentation — A/B design, power analysis, and readouts
- Forecasting — Forecasts with backtests for planning cycles
- Reproducible notebooks — Analysis that can be re-run by your team
- SQL feature engineering — Warehouse features with review notes
- Model cards & handoff — Limits, sources, and owners documented
- XGBoost & gradient boosting — Tabular models for business use cases
- SHAP & model explainability — Driver explanations for stakeholders
Outcomes teams see
- 5+ years — Average applied data-science experience
- 3–6 weeks — Typical time to first experiment readout
- Documented assumptions — Power analysis and success metrics before launch
- Reproducible pipelines — Training and scoring work your team can audit
Next step
Share the decision you need support for and the data access available. We align engagement model and send scientists ready to deliver usable analysis.