Data Scientist

Python plus the scientific stack - the everyday tools of the trade.

Distributions, hypothesis testing, confidence intervals, and regression - the core reasoning of the role.

Pull and shape data straight from the warehouse; often the first thing you do each day.

Cleaning, profiling, and visualizing data to form and test hypotheses before modeling.

Communicate findings clearly - dashboards and charts in Tableau or Power BI.

Model data that moves over time - a common ask in demand, finance, and ops.

Supervised and unsupervised learning, model evaluation, and avoiding leakage and overfitting.

Neural networks and a framework (PyTorch or TensorFlow) for problems classical ML can't reach.

Increasingly expected - text analysis, embeddings, and using LLMs for extraction and classification.

Design experiments and reason about cause vs. correlation - where data science drives decisions.

Work at scale with a cloud platform and a modern warehouse.

Hand models off to production reliably with basic MLOps and experiment tracking.