Pricing Data Scientist...

Job Description

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.

Responsibilities

• 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.
  
  Tech stack
  
  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.
  
  What you'll bring
  
  
• 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.
  
  Desired Skills and Experience
  
  Experience & skills checklist
  
  3-5 + yrs optimisation/recommender work at production scale (dynamic pricing, yield, marketplace matching).
  
  Mathematical optimisation know-how - LP/MIP, heuristics, constraint tuning, objective-function design.
  
  Python toolbox: pandas, NumPy, scikit-learn, PyTorch/TensorFlow; clean, tested code.
  
  Cloud ML: hands-on with AWS SageMaker plus exposure to Azure ML; Docker, Git, CI/CD, Terraform.
  
  SQL mastery for heavy-duty data wrangling and feature engineering.
  
  Experimentation chops - offline metrics, online A/B test design, uplift analysis.
  
  Production mindset: containerise models, deploy via Airflow/ADF, monitor drift, automate retraining.
  
  Soft skills: clear comms, concise docs, and a collaborative approach with DS, Eng & Product.
  
  Bonus extras: Spark/Databricks, Kubernetes, big-data panel or ad-tech experience.