From software start-up to
taming big data.

Dynamic data duo Alex & Stephen went from developers for a tech start-up to Data Scientists at a UK telecommunications company.

After a short spell as Python developers at a tech start-up, Alex & Stephen made the step up to the data science team of the UK's largest mobile and broadband network provider to assist their model building processes.


Reporting directly to a team of senior data scientists, both were tasked with creating two machine learning models for a marketing campaign to target specific customers who are most likely to purchase a service from the client.

The Challenge

The client had the following challenges:

  • Efficiency issues

With such a large subscriber base, one of the main challenges the client faced was the sheer magnitude of datasets, meaning running scripts would take significant periods of time.

  • Data-informed stakeholders

Different stakeholders within the business needed data to make informed decisions, a process that involved manipulating and building large datasets and evaluating and presenting the results to the wider team.

  • Customer attraction

The client wanted to know how they could target their campaigns to the appropriate customers and increase engagements using historical data. This would need to be done by creating machine learning models using extremely large volumes of data.


The Solution

Alex and Stephen's responsibilities included:


Clarify project requirements

Communicating with key stakeholders to clarify the requirements and ensure that all criteria was met for the model.


Refine and clean datasets

Utilising engineering techniques to identify the most important features for the models to reduce the size of datasets.


Build and evaluate model

Once the model was created, they had to evaluate the performance of the model by looking at its precision.

The Outcome

1. Inform business decision making

The models built by Alex and Stephen helped senior stakeholders to make informed decisions and from this the marketing department used the model outputs to target and retain customers.

2. Optimised model performance

Stephen and Alex were delighted to learn that their model performed extremely well in comparison to previous models, leading to the client approving its industrialisation.

3. Increased automation

With the model now automated, stakeholders can now see the model results on a regular basis.

4. Process adoption

The data showed clear and positive trends, demonstrating an enthusiastic adoption of the new systems and processes by colleagues.

"Alex & Stephen are extremely respectful, diligent, and keen to learn and understand new processes. Every task that was set of them was completed in a timely manner. I was also impressed by the confidence displayed in the general skills and attitude displayed to projects."

Senior Data Scientist

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