Price Optimisation Data Scientist

Job Title
Predictive Price Modelling DS

Logistics

• Contract length: 6 months

• Day rate: £500-£550 (Outside IR35)

• Location: Hybrid-2 days/week in London office

Client Context
Work with top-tier retailers and e-commerce brands that demand scientifically rigorous pricing strategies. Your models will directly inform multi-million-pound margin and revenue decisions.

Role Overview
As a Price Modelling Specialist, you'll lead the design, development and deployment of predictive pricing and elasticity models. You'll translate data into dynamic pricing rules that maximise revenue, test their impact in-market, and package insights for commercial leaders.

Key Responsibilities

• Develop and maintain price-elasticity, demand-forecasting and revenue-optimisation models in Python

• Design, execute and analyse A/B tests and quasi-experimental frameworks to validate pricing strategies

• Build automated pipelines to refresh price recommendations in near real-time

• Integrate model outputs into BI dashboards and reporting workflows

• Present clear, actionable pricing insights to commercial and executive stakeholders

Must-Haves

• Master's in Statistics, Econometrics, Data Science or related quantitative field

• Expert in Python (pandas, scikit-learn) and advanced SQL for large-scale data manipulation

• Proven track record running A/B tests or causal analyses on pricing levers

• Excellent communication-able to explain complex models to non-technical audiences

• Full UK work authorisation

Desirable

• Familiarity with production ML workflows (version control, CI/CD)

• Background in retail or e-commerce pricing platforms

Technical Toolbox

• Modelling & data: Python, SQL

• Experimentation: A/B testing frameworks

• Containerisation: Docker

• BI & warehousing: Snowflake (or equivalent), Tableau/Power BI

• Cloud: AWS, GCP or Azure
  

Desired Skills and Experience
3+ years developing price-elasticity and demand-forecasting models in Python
Designing and analysing A/B tests and quasi-experiments to validate pricing strategies
Building automated pipelines for real-time price recommendation updates
Integrating model outputs into Tableau or Power BI dashboards
Communicating complex pricing insights to commercial and executive stakeholders
Working with Docker and cloud platforms (AWS, GCP or Azure) for scalable deployments