Pricing & Revenue Data Scientist

Outside IR35 | £400-450 per day | 3-month initial term | Hybrid London (2-3 days on-site)

A global marketing-data organisation is upgrading the engine that matches millions of survey invitations to the right respondents. Your task: treat the matching pipeline as a full-scale optimisation problem and raise both accuracy and yield.

• Model optimisation - refactor and improve existing matching/segmentation models; design objective functions that balance cost, speed and data quality.

• Experimentation - set up offline metrics and online A/B tests; analyse uplift and iterate quickly.

• Production delivery - build scalable pipelines in AWS SageMaker (moving to Azure ML); containerise code and hook into CI/CD.

• Monitoring & tuning - track drift, response quality and spend; implement automated retraining triggers.

• Collaboration - work with Data Engineering, Product and Ops teams to translate business constraints into mathematical formulations.

1. Morning stand-up: align on performance targets and new constraints.

2. Data dive: explore panel behaviour in Python/SQL, craft new features.

3. Modelling sprint: run hyper-parameter sweeps or explore heuristic/greedy and MIP/SAT approaches.

4. Deployment: ship a model as a container, update an Airflow (or Azure Data Factory) job.

5. Review: inspect dashboards, compare control vs. treatment, plan next experiment.

Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow)
SQL (Redshift, Snowflake or similar)
AWS SageMaker ? Azure ML migration, with Docker, Git, Terraform, Airflow / ADF
Optional extras: Spark, Databricks, Kubernetes.

• 3-5+ years building optimisation or recommendation systems at scale.

• Strong grasp of mathematical optimisation (e.g., linear/integer programming, meta-heuristics) as well as ML.

• Hands-on cloud ML experience (AWS or Azure).

• Proven track record turning prototypes into reliable production services.

• Clear communication and documentation habits.