About the role
Data Technology scope, design and build stable, secure, cost-effective and highly automated Data solutions, enabling business growth and effective risk management they are the Go-
To Team as subject matter experts who understand our datasets and provide guidance on data lineage from core systems to data warehouses, and in supporting analytic and reporting tools.
What will you be doing?
Having very good business understanding on a high level and understanding the needs of stakeholders.
Communicating with different Barclays functions data science results and machine learning outcomes on a very high level explaining at the same time the impact on the Buisness.
Generating strategic insights from multiple, often complex and unstructured raw data sets, using your expert understanding of analysis techniques.
Work across the business, supporting strategy development and leading initiatives that will provide insights and improve the accuracy and availability of important data.
Opportunity to work with very new technologies, whilst shaping the development of a new team, as well as your career.
What we’re looking for :
Apply Data Science techniques to optimise and improve business performance for the Transaction Cycles (Merchant Services, Fraud, Contact Centre and Cards Platform) and Business Unit (Cards & Payments) supported by the team for specific business problems and use cases (eg.
Fraud, Complaints, Customer Journey etc.)
Define, and promote re-usable, scalable, maintainable Data Science solutions considering trade-off for cost vs benefit
Work seamlessly with Business or Function teams to deliver initiatives agreed with the business
The successful candidate should pro-actively keep their own specialist skills and knowledge at leading edge standard in order to fully fulfil their role
Communicate the importance of solution design in a clear and credible way at all levels.
Able to produce productionizable python code.
Develop a successful data science profile using the team resources.
Able to understand and contribute to new concepts that lead to major deliverables.
Update the entire team with new technologies and new methods that are presented in conferences, publications online etc.
Skills that will help you in the role :
Very good programming skills in Python, and Scala
Good understanding of maths and statistics (e.g. regression, optimization, probabilities) and able to write efficiently code on these topics.
Good experience with of basic machine learning concepts (methods of the python package Scikit-learn logistic regression, random forest, nearest neighbors, K-means etc.)
Good experience with deep neural networks and gradient boosting methods (XGBoost, LightGBM)
Good understanding of business and operational processes
Where will you be working?
The role is open across 2 different locations giving you the option to work across either our London office in Canary Wharf or our unique tech hub in Northampton.
Interested and want to know more about Barclays? Visit for more details.
Everything we do is shaped by the five values of Respect, Integrity, Service, Excellence and Stewardship. Our values inform the foundations of our relationships with customers and clients, but they also shape how we measure and reward the performance of our colleagues.
Simply put, success is not just about what you achieve, but about how you achieve it.
We aim to foster a culture where individuals of all backgrounds feel confident in bringing their whole selves to work, feel included and their talents are nurtured, empowering them to contribute fully to our vision and goals.
Our customers are unique. The same goes for our colleagues. That's why at Barclays we offer a range of benefits, allowing every colleague to choose the best options for their personal circumstances.
These include a competitive salary and pension, health care and all the tools, technology and support to help you become the very best you can be.
We are proud of our dynamic working options for colleagues. If you have a need for flexibility then please discuss this with us.