Build meaningful technology with accountable teams

Job description

The Data Scientist will develop predictive, classification, forecasting and optimisation solutions using governed enterprise data.

The role includes problem framing, feature development, evaluation, explainability and collaboration with engineers to move successful models into production.

Key responsibilities

• Translate business problems into testable analytical and machine-learning hypotheses.
• Prepare data, engineer features and build reproducible modelling pipelines.
• Select appropriate evaluation metrics and compare model performance against practical baselines.
• Document assumptions, limitations, bias considerations and model behaviour.
• Work with data and platform engineers on deployment, monitoring and model lifecycle controls.

Qualifications

• 3–6 years of professional data-science, machine-learning or advanced analytics experience.
• Strong Python, SQL and statistical modelling skills.
• Hands-on experience with supervised and unsupervised machine-learning techniques.
• Understanding of model evaluation, feature engineering and reproducible experimentation.

Preferred qualifications

• Experience with MLflow, Databricks, Azure ML or comparable MLOps tooling.
• Time-series, NLP or optimisation experience.
• Experience deploying models into business or digital-product workflows.

Benefits & employment terms

• Compensation, leave, pension and any role-specific benefits are confirmed during the recruitment process and stated in the written offer.
• Any client-site, travel, security-screening or right-to-work requirements are confirmed before appointment.

Nature of working style

• Data scientists work with business owners, analysts, data engineers and application teams throughout the model lifecycle.
• Model quality and business usefulness are reviewed together rather than treating offline accuracy as the only success measure.

Location

London-based hybrid role with project-specific client collaboration.