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# Contract Data Scientist - Search / Ranking / Recommendation

- **Company:** Harnham
- **Location:** London, UK (Remote available)
- **Experience:** Expert
- **Salary:** £104,000.0 - £124,800.0
- **Contract:** Temporary contract
- **Skills:** A/B Testing, Amazon Web Services, Data Analysis, Python (Programming Language), Recommender Systems, Standard Sql, Transaction Data, Snowflake, Xgboost, Streamlit Framework
- **Published:** October 10, 2026
- **Apply:** https://www.reed.co.uk/jobs/contract-data-scientist-search-ranking-recommendation/57442891

## About the Role

* Hands-on experience with search ranking / search order / sort order
 * Hands-on experience with recommendation / recommender systems
 * Strong Python and SQL
 * Snowflake
 * AWS
 * Strong A/B testing / experimentation
 * Experience measuring ranking or recommendation changes against commercial KPIs
 * Strong hands-on Data Science background

Nice to have:

 * Learning-to-rank / re-ranking
 * Metaflow
 * XGBoost / LightGBM
 * Propensity modelling
 * Optimisation
 * PyMC
 * Streamlit
 * Cold-start recommendation
 * Travel, ecommerce, marketplace or booking-platform experience

## Description

I'm working with a major consumer travel business looking to bring in a hands-on Data Scientist to support a project focused specifically on search order, ranking and recommendation.

The business manages a large inventory of holiday properties, and the core problem is deciding which properties should appear, in what order, for a given customer search.

This is not a generic Data Science role. Previous experience with search ranking / sort order / recommendation systems is essential.

The role will involve:

 * Improving the search order / ranking logic used to surface properties to customers
 * Building and refining recommendation and ranking models
 * Working across ranking by destination, country, brand, park and individual property / unit level
 * Running A/B tests and experiments to measure the impact of ranking changes
 * Measuring performance against customer and commercial KPIs
 * Analysing how factors such as price, brand, region and country affect sort order performance
 * Building out a broader dashboard / KPI framework
 * Working with behavioural and transactional data
 * Feeding findings and recommendations back to product and business stakeholders