Pricing & Revenue Data Scientist

ZipRecruiter; London; Hybrid; GBP 150,000 - 200,000; Full time

Job summary

A global marketing-data organisation seeks a Senior Data Scientist specializing in optimisation. You will enhance matching models, lead experimentation efforts, and deploy scalable solutions in a hybrid work environment. Ideal candidates will have extensive experience in cloud ML, Python, and a strong mathematical foundation in optimization techniques.

Qualifications

• 3-5+ years building optimisation or recommendation systems at scale.
• Strong grasp of mathematical optimisation techniques.
• Hands-on cloud ML experience with AWS or Azure.

Responsibilities

• Refactor and improve existing matching/segmentation models.
• Set up offline metrics and online A/B tests.
• Build scalable pipelines and monitor quality outcomes.

Skills

Optimisation

Recommendation Systems

Mathematical Optimisation

Cloud ML

Python

SQL

Experimentation

Tools

AWS SageMaker

Azure ML

Docker

Git

Terraform

Airflow

Job description

Job Description

Senior Data Scientist - Optimisation (Contract)

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

The brief

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.

Core 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.
  
  

A typical day

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.
   
   

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.