Our amazing Data team has grown by over 500% in 18 months and there is no end in sight. We are currently recruiting for a significant number of roles which will be mainly based in London, but the location may be flexible.

BJSS data team members


Data Architect

Are you able to design and deliver strategic data platforms for our clients?

Data Engineer

Can you help enterprises deploy production ready Big Data or Cloud systems?

Data Scientist 

Do you understand how businesses can use data to increase their revenues?

BI Developer 

Can you create user friendly dashboards and reports with actionable insight?


Great Opportunities

We enable our team members to work across a variety of industries, from retail and finance to public sector and healthcare, as well as commodities and utilities. You won’t be pigeon- holed into one sector, so you can build your experience and portfolio with multiple interesting clients over time.

Great Training

Our team members come from different backgrounds and with different expertise, not everyone was a data expert before they joined us. So don’t worry if your current job title doesn’t match the role you want to apply for. We offer great training to our new starters and we can help you fill in any gaps and expand your knowledge and skills.

Great Benefits

We can offer you a competitive salary, help with professional & industry qualifications as well as healthcare and income protection. We also offer flexible working hours.


BJSS employee – 7 months

Since I have started at BJSS, I have been working at a large commodity company where I am helping to build up their Data Science capability from scratch. This includes the recruitment of Data Scientists and other data-related roles, but also strategical tasks like identifying and scoping valuable Data Science projects and setting up the appropriate infrastructure.

The first projects we were working on was a customer segmentation and an early warning system for customer churn for their B2B business unit. The insights from those models are used by Sales departments use to offer a more personalised customer experience in product and price negotiations and define strategies to prevent churn.

Currently, we are working in the B2C area where we are trying to understand how a customer’s shopping behaviour is driven by the company’s product offering vs external market conditions. Using Databricks on Microsoft Azure, we’re developing a forecasting model to predict customer demand that will enable us to optimise and target product offerings.


BJSS employee – 2 years

Since working for BJSS, I have delivered four different projects in finance and in the oil and gas sector. My job role has been creating innovative solutions from proof of concept to delivering minimal viable product and continuous improvements through Enterprise Agile.

I have helped to deliver a Bayesian framework capable of exploiting the multi-level (hierarchy) of an accounting system, to help manage cash flow and working capital across thousands of customer accounts, using different underlying information system due to the prevention of full linkage. Within this project the Data team delivered a non-parametric cluster-based regression algorithm.  We explored a combined solution using the Bayesian framework, which can exploit multiple data sources at different granularity level and calibrate forecast from linear regression.  Within this project I has exposure to R and Azure ML studio as REST API.

Within an Oil & Gas client they needed a tool to automatically sift through tens of thousands of valuation transactions of physical oil assets at the end of each business day.  Their goal was to look for valuation transactions that are anomalous or unusual that will subject to another, independent assessment.  Based on up to 500 business days of historical data, we formulated the outlier detection problem as a classification problem and a regression problem. The proposed solution ranks transactions that have extremely high probabilities of being a zero-valued transaction when the observed P&L change is not zero; and vice versa.  For this project, I used Python, XBOOST and Power BI.


BJSS employee – 1 year

I have been involved with two main client projects since starting at BJSS.

The first was a research collaboration between three international banks. Our aim was to explore different data mining techniques to find the most common journeys customers took when making changes to their mortgage payments. My team decided to try out the knowledge graph platform Neo4j for the data mining, combined with Python on Jupyter notebooks for further analysis.

The second project is looking at how to monitor models going into production to ensure that they are ‘safe’. We want to implement control measures which flag to the run team if something happens which could impact the model, and which need to be acted on to keep the model accurate and therefore the business safe. We are developing these controls using Python and will be running them alongside their relevant models through a Jenkins pipeline.