Data Scientist

Robert Walters is pleased to be partnering with BNP Paribas in their search for a Data Scientist. At BNP Paribas Personal Finance are proud to be a Top Employer UK, providing a great place to work in an open and inclusive environment. Providing simple andstraightforward consumer finance solutions, they work with many well-known retailers and brands to help their customers finance their purchases in a responsible way.

As part of the Risk & Analytics team, you will be evaluating and improving processes and products, collaborating with Analysts and Engineers to transform data into meaningful insights.

**What will you be doing?**
- Developing, validating and maintaining explainable machine learning models, exploring and adopting new data sources that improve efficacy of models.
- Extracting, cleansing and analysing large and complex datasets, structuring, manipulating and staging to produce optimal decisions.
- Conducting analysis, including gathering data and specifying requirements in collaboration with business stakeholders.
- Identifying opportunities where advanced analytics can be solved with data science.
- Selling the vision of advanced analytics and data-based decision making to wider stakeholders. Managing and engaging with both technical and non-technical teams.
- Building and maintaining relationships with Central Risk, Finance, Marketing and external teams, whilst delivering new lending strategies within the agreed risk appetite.
- Identifying areas of cost reduction, service and process improvements that will results in adding value to the business whilst treating customers fairly.

**What will you need?**
- Educated to Degree level or higher in Quantitative field (Statistics, Mathematics, Computer Science, ML, AI etc).
- Experience designing and building machine learning models.
- Strong coding experience in at least one of the following programming. languages; SAS, SQL or Python.
- Strong experience in Microsoft Excel.
- Strong analytical background, with a history of presenting findings to senior stakeholders both internally and externally.
- A track record of demonstrating a continuous improvement approach to data processes, streamlining any inefficiencies.
- An understanding of the credit lifecycle, credit bureau, financial products or economic conditions.
- An interest and hunger to pursue machine learning.

Please acknowledge, if you do not hear back from myself or my team in 72 hours, you have been unsuccessful on this occasion.