Data Scientist VA

Responsibilities

• Frame business questions as analytical problems and agree the metrics that define success before modelling.
• Explore, clean and prepare data with Python or R, SQL and libraries such as pandas, NumPy and scikit-learn.
• Build, validate and tune predictive models for churn, demand forecasting, pricing, segmentation, recommendation or risk.
• Design and analyse experiments and A/B tests with sound statistical methods and honest reporting of uncertainty.
• Deploy models to production with engineers using tools such as MLflow, Docker, SageMaker or Vertex AI, and monitor their performance over time.
• Communicate findings through clear visualisations, written summaries and presentations for non-technical audiences.
• Work with data engineers to define the features, datasets and pipelines that models need.
• Apply responsible data practices including privacy, bias checks and model documentation.
• Maintain reproducible code and notebooks in Git with tests and documentation.
• Stay current with methods, including large language models, and assess where they genuinely add value for the client.