The Data Scientist is the specialist who uses statistics and machine learning to draw forecasts, explanations or automated decisions from data: forecasting demand, detecting fraud, estimating the risk that a customer will churn, recommending a product.
They frame the problem with business teams, explore the data, choose and train a model (Python, scikit-learn, PyTorch), then measure whether it beats a simple rule. A large part of the work consists of validating results and explaining their limits.
The job has specialized. Putting models into production and monitoring them often falls to the ML Engineer or MLOps Engineer, and integrating existing LLMs to the AI Engineer. The Data Scientist keeps the analytical and statistical core: experimentation, causality, custom modeling. In deeptech and industry, they also work on sensor, image or simulation data.
Why it matters when hiring
The title covers very different profiles, from near-analyst to researcher. Be clear about whether you expect models in production, one-off studies or research. The strong signal on a resume: a model shipped to production with measured impact. In an interview, ask how they chose their evaluation metric and what they did when the model degraded.