Job description: Senior Data Scientist
Client’s Advanced Analytics & AI function turns operational and commercial data into decisions across parking, aeronautical, retail and airport operations. As a Senior Data Scientist (Onshore) you own our highest value data science use cases end-to-end — from framing the problem with business owners through to deploying and monitoring production models — and act as the trusted technical advisor bridging the business and our onshore/offshore delivery team.
KEY RESPONSIBILITIES:
- Lead delivery of forecasting and optimisation use cases such as car-park occupancy and price-elasticity modelling, valet resource optimisation, 18-month and 5-year passenger (PAX) forecasts, ML security-screening forecasts, and retail PSR and cross-sell models.
- Partner directly with business owners across Parking, Commercial/Aero, Retail and Operations to frame problems, define success measures, and translate model outputs into pricing, capacity, staffing and revenue decisions.
- Design, build, validate and productionise models in Python on our data science platform (Azure Machine Learning), integrated with Snowflake.
- Own model quality and the full lifecycle — feature engineering, explainability and what-if analysis, batch prediction, and production monitoring for data drift and model health.
- Present forecasts, insights and recommendations to senior stakeholders and executives, and run scenario analysis to support high-stakes decisions (e.g. capacity build vs no-build, pricing strategy).
- Set technical direction and mentor onshore and offshore data scientists, review work and lift delivery standards.
SKILLS & EXPERIENCE — ESSENTIAL:
- 7+ years’ applied data science, with a track record of models deployed to production and adopted by the business.
- Expert in Python (pandas, scikit-learn and related ML/stats libraries) and SQL, with a strong foundation in time series forecasting, regression, classification and clustering.
- Hands-on experience with an enterprise ML platform (Azure ML or equivalent AutoML) and a cloud data warehouse (Snowflake or equivalent).
- Proven MLOps discipline — model deployment, batch-prediction pipelines, monitoring, drift detection and retraining.
- Working experience on Microsoft Azure (e.g. Azure ML, Azure DevOps, storage and compute services).
- Excellent stakeholder engagement and data storytelling — able to turn technical results into commercial decisions and present with confidence to executives.
- Degree in a quantitative discipline (Statistics, Mathematics, Computer Science, Engineering or Data Science) or equivalent experience.
DESIRABLE:
- Aviation, transport, or pricing / revenue-management and forecasting-heavy operational domains.
- Power BI, explainable AI, optimisation, and A/B testing frameworks.
- Familiarity with Responsible AI governance and privacy-aware analytics.
- Experience leading or mentoring distributed onshore/offshore teams.
OUR TOOLS & ENVIRONMENT
- Python · SQL · Microsoft Azure Machine Learning · Snowflake · Power BI · Azure Cloud