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A Day in the Life of a Data Scientist

A Day in the Life of a Data Scientist

To support our continued growth, Analytics Engines are searching for a data scientist to join our team. To provide interested individuals with some insight into the role and what life at Analytics Engines is like, we spoke with Lead Data Scientist, Liam Brannigan.

Beginning the day

I begin the day by checking in with my colleagues during our stand-up. We work in small, closely integrated client teams and it is important for me to keep track of what is happening on the database development as well as the front-end app.

The stand-up also gives me an opportunity to set my priorities for the day and make sure that everyone knows how the data science components of a particular project are developing.

Solution Development

Once stand-up is over, I make myself a coffee and begin work on high-level solution development. In projects where the goal is well-defined, this might be a matter of working through an appropriate model loss function to meet a specific client objective.

In many projects, our clients are looking for guidance from us as to what problems they could potentially solve using the data that is available to them. In these cases, solution development focuses first on loading and cleaning their datasets.

Once this is done, I can then focus on basic analysis and visualisation of their data. We work across a number of industries, so I often supplement my understanding of the data and industry with academic research papers.

In doing so, I am able to develop a better understanding of their data domain and as a result, I can have greater confidence in the features and presentation of the visualisations.

At this stage of data investigation, we also decide which problems to solve. This choice is generally the most crucial point in the whole process. Quite often, we find ourselves identifying a client problem that they did not realise they could solve.

Once we have agreed on an appropriate problem to solve, we begin the classical machine learning stage of developing the training dataset and evaluating models.

Staying up to date

It is also important that we remain up to date with new developments in the data science space. I personally do this by following leading data science experts on social media and by reading academic papers.

We want to make sure that we are not missing out on any major new developments. These new developments may be related to new algorithms such as the transformers models that have had a major impact on text analysis.

These developments may also be related to how data science projects are delivered. For example, we have found that collaboration between data scientists is greatly enhanced using a combination of DVC ( and Docker.

Life at Analytics Engines

I have worked at Analytics Engines for several years now. I initially joined the company as a data scientist after transitioning from a career as a Physical Oceanographer. During my time here, I have had the opportunity to develop my expertise further and been afforded an effective work-life balance.

If you are interested in applying or if you have any questions about the role, please feel free to reach out to me on Twitter at @braaannigan.

Work with Us

If you would be interested in working with us, apply for the position of Data Scientist using the link below.

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by PJ Kirk

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