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
